Fish population variability and fisheries activities are closely linked to weather and climate dynamics. While weather at sea directly affects fishing, environmental variability determines the distribution, migration, and abundance of fish. Fishery science grew up during the last century by integrating knowledge from oceanography, fish biology, marine ecology, and fish population dynamics, largely focused on the great Northern Hemisphere fisheries. During this period, understanding and explaining interannual fish recruitment variability became a major focus for fisheries oceanographers. Yet, the close link between climate and fisheries is best illustrated by the effect of “unexpected” events—that is, nonseasonal, and sometimes catastrophic—on fish exploitation, such as those associated with the El Niño–Southern Oscillation (ENSO). The observation that fish populations fluctuate at decadal time scales and show patterns of synchrony while being geographically separated drew attention to oceanographic processes driven by low-frequency signals, as reflected by indices tracking large-scale climate patterns such as the Pacific decadal oscillation (PDO) and the North Atlantic Oscillation (NAO). This low-frequency variability was first observed in catch fluctuations of small pelagic fish (anchovies and sardines), but similar effects soon emerged for larger fish such as salmon, various groundfish species, and some tuna species. Today, the availability of long time series of observations combined with major scientific advances in sampling and modeling the oceans’ ecosystems allows fisheries science to investigate processes generating variability in abundance, distribution, and dynamics of fish species at daily, decadal, and even centennial scales. These studies are central to the research program of Global Ocean Ecosystems Dynamics (GLOBEC). This review presents examples of relationships between climate variability and fisheries at these different time scales for species covering various marine ecosystems ranging from equatorial to subarctic regions. Some of the known mechanisms linking climate variability and exploited fish populations are described, as well as some leading hypotheses, and their implications for their management and for the modeling of their dynamics. It is concluded with recommendations for collaborative work between climatologists, oceanographers, and fisheries scientists to resolve some of the outstanding problems in the development of sustainable fisheries.
ISI Document Delivery No.: 174OX Times Cited: 2 Cited Reference Count: 49 Cited References: [Anonymous], 2011, CLIM CHANG PAC SCI A, V1 Aqorau T., 2009, International Journal of Marine and Coastal Law, V24, P557, DOI 10.1163/157180809X455647 Bell JD, 2009, MAR POLICY, V33, P64, DOI 10.1016/j.marpol.2008.04.002 Bell J.D., 2011, VULNERABILITY TROPIC Brown J. R., 2010, J CLIMATE, V24, P1565 Brown JN, 2013, CLIMATIC CHANGE, V119, P147, DOI 10.1007/s10584-012-0603-5 Bruno J. F., 2007, PLOS ONE, V2, P1 Cheung WWL, 2013, NAT CLIM CHANGE, V3, P254, DOI 10.1038/NCLIMATE1691 Cia W., 2012, NATURE, V488, P365 Clapcott JE, 2012, FRESHWATER BIOL, V57, P74, DOI 10.1111/j.1365-2427.2011.02696.x Cochrane KL, 2011, FISH FISH, V12, P275, DOI 10.1111/j.1467-2979.2010.00392.x Collins M, 2010, NAT GEOSCI, V3, P391, DOI 10.1038/NGEO868 Cravatte S, 2009, CLIM DYNAM, V33, P565, DOI 10.1007/s00382-009-0526-7 Della Patrona L, 2011, AQUACULT ENV INTERAC, V2, P27, DOI 10.3354/aei00028 Durack PJ, 2012, SCIENCE, V336, P455, DOI 10.1126/science.1212222 Ellison JC, 2009, WETL ECOL MANAG, V17, P169, DOI 10.1007/s11273-008-9097-3 ElSayed AFM, 2006, TILAPIA CULTURE, P1, DOI 10.1079/9780851990149.0001 Ganachaud A, 2013, CLIMATIC CHANGE, V119, P163, DOI 10.1007/s10584-012-0631-1 Gillett R., 2010, FUTURE PACIFIC ISLAN Gillett R, 2009, FISHERIES EC PACIFIC Grafton RQ, 2010, MAR POLICY, V34, P606, DOI 10.1016/j.marpol.2009.11.011 Hoegh-Guldberg O, 2007, SCIENCE, V318, P1737, DOI 10.1126/science.1152509 Karnauskas KB, 2012, NAT CLIM CHANGE, V2, P530, DOI 10.1038/NCLIMATE1499 Knecht R. W., 1998, INTEGRATED COASTAL O Lehodey P, 2008, PROG OCEANOGR, V78, P304, DOI 10.1016/j.pocean.2008.06.004 Lehodey P, 2013, CLIMATIC CHANGE, V119, P95, DOI 10.1007/s10584-012-0595-1 Lehodey P, 1997, NATURE, V389, P715, DOI 10.1038/39575 Longhurst A, 2006, ECOLOGICAL GEOGRAPHY Vermeer M, 2009, P NATL ACAD SCI USA, V106, P21527, DOI 10.1073/pnas.0907765106 Meehl GA, 2007, B AM METEOROL SOC, V88, P1383, DOI 10.1175/BAMS-88-9-1383 Murawski SA, 2011, ICES J MAR SCI, V68, P1368, DOI 10.1093/icesjms/fsr086 Nakicenovic N., 2000, SPECIAL REPORT EMISS Newton K., 2007, CURR BIOL, V17, P1 Nilsson GE, 2012, NAT CLIM CHANGE, V2, P201, DOI [10.1038/NCLIMATE1352, 10.1038/nclimate1352] Orr JC, 2005, NATURE, V437, P681, DOI 10.1038/nature04095 Palmer MA, 2008, FRONT ECOL ENVIRON, V6, P81, DOI 10.1890/060148 Parker LM, 2011, MAR BIOL, V158, P689, DOI 10.1007/s00227-010-1592-4 Patra RW, 2007, ENVIRON TOXICOL CHEM, V26, P1454, DOI 10.1897/06-156R1.1 Pinca S., 2010, REGIONAL ASSESSMENT Pomeroy RS, 2011, SMALL-SCALE FISHERIES MANAGEMENT: FRAMEWORKS AND APPROACHES FOR THE DEVELOPING WORLD, P1, DOI 10.1079/9781845936075.0000 Ponia B., 2010, REV AQUACULTURE PACI Ricel JC, 2011, ICES J MAR SCI, V68, P1343, DOI 10.1093/icesjms/fsr041 Sen Gupta A, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051447 Smith P. T., 2007, ACIAR MONOGRAPH, V125 Solomon S., 2007, IPCC CLIMATE CHANGE Southgate PC, 2008, PEARL OYSTER, P1 Sunda WG, 1997, NATURE, V390, P389, DOI 10.1038/37093 Welladsen HM, 2010, MOLLUSCAN RES, ...
Abstract. Model intercomparison studies in the climate and Earth sciences communities have been crucial to building credibility and coherence for future projections. They have quantified variability among models, spurred model development, contrasted within- and among-model uncertainty, assessed model fits to historical data, and provided ensemble projections of future change under specified scenarios. Given the speed and magnitude of anthropogenic change in the marine environment and the consequent effects on food security, biodiversity, marine industries, and society, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. Here, we describe the Fisheries and Marine Ecosystem Model Intercomparison Project protocol version 1.0 (Fish-MIP v1.0), part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is a cross-sectoral network of climate impact modellers. Given the complexity of the marine ecosystem, this class of models has substantial heterogeneity of purpose, scope, theoretical underpinning, processes considered, parameterizations, resolution (grain size), and spatial extent. This heterogeneity reflects the lack of a unified understanding of the marine ecosystem and implies that the assemblage of all models is more likely to include a greater number of relevant processes than any single model. The current Fish-MIP protocol is designed to allow these heterogeneous models to be forced with common Earth System Model (ESM) Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs under prescribed scenarios for historic (from the 1950s) and future (to 2100) time periods; it will be adapted to CMIP phase 6 (CMIP6) in future iterations. It also describes a standardized set of outputs for each participating Fish-MIP model to produce. This enables the broad characterization of differences between and uncertainties within models and projections when assessing climate and fisheries impacts on marine ecosystems and the services they provide. The systematic generation, collation, and comparison of results from Fish-MIP will inform an understanding of the range of plausible changes in marine ecosystems and improve our capacity to define and convey the strengths and weaknesses of model-based advice on future states of marine ecosystems and fisheries. Ultimately, Fish-MIP represents a step towards bringing together the marine ecosystem modelling community to produce consistent ensemble medium- and long-term projections of marine ecosystems.
International audienceThe modeling of mid-trophic organisms of the pelagic ecosystem is a critical step in linking the coupled physical–biogeochemical models to population dynamics of large pelagic predators. Here, we provide an example of a modeling approach with definitions of several pelagic mid-trophic functional groups. This application includes six different groups characterized by their vertical behavior, i.e., occurrence of diel migration between epipelagic, mesopelagic and bathypelagic layers. Parameterization of the dynamics of these components is based on a temperature-linked time development relationship. Estimated parameters of this relationship are close to those predicted by a model based on a theoretical description of the allocation of metabolic energy at the cellular level, and that predicts a species metabolic rate in terms of its body mass and temperature. Then, a simple energy transfer from primary production is used, justified by the existence of constant slopes in log–log biomass size spectrum relationships. Recruitment, ageing, mortality and passive transport with horizontal currents, taking into account vertical behavior of organisms, are modeled by a system of advection–diffusion-reaction equations. Temperature and currents averaged in each vertical layer are provided independently by an Ocean General Circulation Model and used to drive the mid-trophic level (MTL) model. Simulation outputs are presented for the tropical Pacific Ocean to illustrate how different temperature and oceanic circulation conditions result in spatial and temporal lags between regions of high primary production and regions of aggregation of mid-trophic biomass. Predicted biomasses are compared against available data. Data requirements to evaluate outputs of these types of models are discussed, as well as the prospects that they offer both for ecosystem models of lower and upper trophic levels
This is the author's manuscript for a work that has been accepted for publication. Changes resulting from the publishing process, such as copyediting, final layout, and pagination, may not be reflected in this document. The publisher takes permanent responsibility for the work. Content and layout follow publisher's submission requirements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.