Potential consequences of climate change for primary production and fish production in large marine ecosystems -part III
(a) Predicted effects of climate change on unexploited marine ecosystems
The predicted bottom-up effects of climate change, in the absence of fisheries exploitation, varied widely among EEZ (figure 1). In general, changes in fish production and biomass density mirrored the changes in primary production and phytoplankton density more strongly than changes in temperature. Although the greatest warming was predicted in the EEZ of China, South Korea and along the east coast of North America, only modest reductions in primary production and phytoplankton density (−6%), occurred in these areas, resulting in small changes in overall biomass density of fish (−6%). The largest predicted reductions in phytoplankton and zooplankton density occurred in EEZ within the Indo-Pacific (Palau, −60%), the Humboldt Current (Peru, Chile/Peru, −35%) and the Canary Current (Madeira) regions and caused similar magnitudes of change in the overall biomass of fish. At the other extreme, the largest increases in phytoplankton and zooplankton biomass density led to the largest increases in fish biomass density. Such was the case for EEZ of the Guinea Current (Ghana, Ivory coast, Togo) and the Nordic shelf seas (Jan-Mayen, Greenland), where overall fish biomass density increases exceeded 30 per cent. In some cases, the predicted effects of changes in phytoplankton biomass density on fish density were countered or enhanced by changes in the detritus and benthic detritivore pathway, in zooplankton, or by a very large increase in temperature.
In the absence of exploitation, the predictions of the dynamic size spectra model were closely correlated with those from the static size-based model (Spearman’s rank correlation, ρ = 0.88; electronic supplementary material, figure S1). Outliers occurred because the more simplistic static model did not account for a benthic detritus energy pathway that was predicted to account for a significant proportion of energy flux in some ecosystems.
(b) Top-down effects: validation of the model with fishing
When fishing mortality was added to the dynamic model for the period 1992–2001, modelled catches from 78 of the 107 EEZ were comparable to reported catches (figure 2a). Because true rates of community-wide fishing mortality and selectivity were not known, we assumed a fishing mortality rate of 0.8 yr−1 for all fished size classes, consistent with fishers heavily exploiting all fish that were present. The greatest discrepancies between predicted and reported catches were those for EEZ within the Indo-Pacific (Indonesia) and Northwest Pacific shelf sea (the Sea of Okhotsk, off Russia) regions, where predicted fisheries catches were more than 5 Mt greater than the mean reported catches. The largest deviations from reported catches were also associated with high interannual variability in both model- and data-based catch estimates, for example, for Peru and Chile EEZ. When catches and predictions were aggregated at the domain level across EEZ, the 50 per cent quantiles of predicted catches were strongly correlated with the 50 per cent quantiles of reported catches (figure 2b; Spearman’s rank was ρ = 0.8 for median catches at the domain level, compared with ρ = 0.63 for mean catches averaged over 1992–2001 at the EEZ level).
Modelled relative growth rates were also realistic and fell within the range of empirical growth rates of fish species from the North Sea (figure 2c). The highest relative growth rates occurred in warm and highly production regions (Bay of Bengal), whereas the slowest relative growth rates occurred in cold and lower production ecosystems (Nordic Shelf Seas), in line with expectations from food availability and temperature effects on growth.
(c) Top-down and bottom-up: relative effects of fishing and climate change
Ecosystems responded differently to the same fishing scenarios in the absence of climate change. At the EEZ level, the disruption to the size spectrum from fishing impacts was smaller when net primary production and mean relative growth rates of fish were higher (figure 3). There was a stronger relationship with the latter because realized fish growth rates integrated the effects of both temperature and food availability. Without fishing and only climate change, there was greater variation in the relationship between the disruption to the size spectrum and primary production owing to the mixed responses and multiple environmental drivers under the climate change scenario. The combined effects of both fishing and climate drivers depended on how heavily fished the community was. Under the low fishing mortality rate of 0.2 yr−1, climate effects dominated the deviation from the unexploited size spectrum, whereas fishing effects dominated when mortality rates were high, 0.8 yr−1.
Across-ecosystem effects of fishing and climate change. Deviation from the unexploited size spectrum versus mean net primary productivity (a,c) and mean relative growth rates of fish (b,d) when ecosystems are subjected to: fishing (black), climate change (grey) and climate and fishing (red). Each point represents an EEZ. Equilibrium results based on time-averaged environmental conditions under each scenario were used. Results shown for (a,b) low (0.2 yr−1) and (c,d) high fishing mortality rates (0.8 yr−1). Note the logarithmic scale markings on all of the axes.
Under heavy fishing pressure, reductions in the numerical density relative to the unexploited steady state were most pronounced at larger body sizes (figure 4) and in the domains where individual growth rates were slowest (e.g. Nordic seas, northwest Atlantic and northeast Atlantic shelf seas). Climate change increased or decreased numerical density and growth rates relative to the unexploited steady state across all sizes. The greatest increases occurred in the Nordic seas, northwest Atlantic, northeast Atlantic shelf seas and Gulf of Guinea and the greatest decreases were in the Humboldt, Canary and California current ecosystems. If increases in primary production occurred, the combined effects of climate and fishing relative to those in an unexploited ecosystem were less than fishing alone, but also resulted in stronger top-down cascading effects along the size spectrum. If reductions in primary production occurred, the response of fishing with climate change was magnified (figure 4).
The effect of heavy fisheries exploitation on the resilience of ecosystems to climate change was examined by comparing the coefficients of variation from the biomass density time-series for the most sensitive ‘large’ fish component of the size spectrum (e.g. across the 80 g–100 kg size range), generated from seasonal and interannual variability within each of the EEZ, scenario time slices and fishing mortalities. Overall, the variability in biomass density increased with higher fishing mortality and when the combined effects of fishing and climate reduced fish production (see the electronic supplementary material, figure S2).
This study is a first, and necessarily simplified, attempt towards understanding the potential consequences of climate change on large marine ecosystems and their fisheries using a dynamical size-based food web approach. We achieve this by linking the trophic ecology of coupled size-structured communities with predicted changes in the physical and biogeochemical environment. The dynamic size-spectrum model predicts a mixture of positive and negative responses in fish biomass density and production that mirror the predicted changes in primary production more strongly than changes in temperature. The results corroborate empirical work by showing that potential marine fisheries production is primarily governed by available primary production [46–48].
Previous studies have predicted 30–70% average increases in potential fish production at high latitudes and decreases of up to 40 per cent in the tropics, based primarily on the effects of warming on species distributional ranges . At a large geographical scale, the findings are broadly similar to those based on the species biogeography approach, even though the present projections are based on completely different mechanisms arising from food web processes widely held to govern empirical patterns of size spectra in the open ocean and shelf seas [24,49].
An advantage here is the inclusion of fishing effects, enabling the relative effects of climate change and fishing to be explored within and across size-structured ecosystems. Changes in primary production  and temperature  affected growth rates and fish production, altering the responses of ecosystems to fishing. Either low primary production or cold-water ecosystems conferred higher susceptibility to fishing effects, due to slow relative growth rates. Cold-water ecosystems with higher seasonal fluctuations have been previously described as less likely to sustain heavy exploitation . Also in keeping with empirical studies of fish populations [52–54] and theoretical size-spectrum models [44,55], fishing effects caused ecosystems to become more variable through time, due to reductions in size structure and shifts towards smaller size and higher growth rates. For the same reason, heavily fished ecosystem states were less resilient to climate change compared with unexploited ecosystem states .
For 1992–2001, our models generated catches and growth rates that were broadly realistic when compared with reported catches and growth rates, keeping in mind that the true community-wide fishing mortality rates within these ecosystems are not well known and the landed catch data may be subject to misreporting and bias . The size-based models with relatively limited parameter demands provided surprisingly good estimates of current catch from some of the EEZ, further emphasizing the dominant role of body size in accounting for patterns of predatory interaction and production in marine ecosystems. The weakness of the size-based perspective is that it does not provide predictions of catches from individual species and account for their responses to fishing, but this has to be considered in the context that long-term predictions of individual species dynamics; even when complex population models are developed, they can be unreliable . For example, although total fish production in an ecosystem may be maintained, there may be significant and unpredictable switches in the species contributing. Because recruitment is not modelled at a population level and was held constant to facilitate cross-comparison, the resilience of the community to fishing is not recruitment limited; it does not consider the negative feedback that can result from the removal of highly fecund, large mature spawning fish. For this reason, the specific ecosystem responses associated with a given value of fishing mortality should not be taken in absolute terms and are presented for comparative purposes.
The approach also ignores energy inputs from sources of primary production other than phytoplankton (such as macroalgae, seagrasses and mangroves). Although these make a relatively small contribution globally, contributing to around 5.5 per cent of total marine primary production , they are locally and regionally important contributors to inshore fish production. If the underlying rules determining the links between primary production and fish production do not change markedly with a changing climate, then our capacity to predict future changes in fish production is largely predicated on our capacity to predict future changes in the primary production and the physical environment. We assumed a universal relationship between temperature and the activation energy of metabolism to predict temperature-dependent changes in feeding rates, which may underestimate the effects of warming. More complex species and size-specific empirical relationships with temperature and activation energies for different processes such as attack rates, handling times have been described in this issue [10,11].
Bearing in mind the earlier-mentioned strengths and limitations, the model results are of potential use for global-scale social–ecological analyses such as country-level metrics of vulnerability to climate change . They may also be useful for establishing levels of threat and uncertainty in specific regions, if combined with other model predictions. For example, in the Indo-Pacific ecosystem, the EEZ surrounding the country of Palau experienced the greatest loss of primary production, potential fisheries production and an increase in susceptibility to overfishing. This region is located within the Indo-Pacific Biodiversity Triangle and has the highest richness of corals, molluscs, crustaceans, finfishes and chondrichthyans in the world. It has some of the highest catches of chondrichthyans, mainly through unregulated fisheries . Our results are likely to underestimate climate impacts in these regions where there will be impacts on other sources of production. Furthermore, we considered only one possible forcing scenario taken from one global climate model, using a time slice approach. To fully quantify projected future changes in fish production, it is also necessary to consider the uncertainties associated with the forcing data used, the temporal and spatial scales, as well as the mechanisms included. A formal model ensemble approach (including alternative physical–biogeochemical and ecological models) combined with detailed empirical ground-truthing and retrospective analyses will improve our capabilities to gauge where the most important uncertainties lie. There is a clear need for much greater understanding of the effects of climate change within regional seas, at more localized scales than considered here, as well the role of human responses to change. Advancing these integrated areas of research alongside improving our mechanistic understanding of complex ecological communities across spatial scales will help to elucidate sustainability of fisheries for the larger human population and warmer oceans of the future [3,45,61].
This work was funded by UK Natural Environment Research Council’s Quantifying and Understanding the Earth System programme as part of the ‘QUEST-Fish’ project. The participation of J.L.B. at the SIZEMIC Workshop in Hamburg, Germany, was supported by the German Research Foundation (JA 1726/3-1), the DFG funded Cluster of Excellence (EXC177) at the University of Hamburg and the European Science Foundation SIZEMIC Network.
One contribution of 17 to a Theme Issue ‘Climate change in size-structured ecosystems’.
This journal is © 2012 The Royal Society
photos are not part of the original study
paper can be downloaded in PDF format for your convenience
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
FAO 2010 The state of world fisheries and aquaculture. Rome, Italy: FAO.Google Scholar
Allison E. H., et al. 2009 Vulnerability of national economies to the impacts of climate change on fisheries. Fish Fish. 10, 173–196. doi:10.1111/j.1467-2979.2008.00310.x (doi:10.1111/j.1467-2979.2008.00310.x)CrossRefGoogle Scholar
Merino G., et al. In press. Can marine fisheries and aquaculture meet fish demand from a growing human population in a changing climate? Glob. Environ. Change (doi:10.1016/j.gloenvcha.2012.03.003)Google Scholar
Rice J. C., Garcia S. M. 2011 Fisheries, food security, climate change, and biodiversity: characteristics of the sector and perspectives on emerging issues. ICES J. Mar. Sci. 68, 1343–1353. doi:10.1093/icesjms/fsr041 (doi:10.1093/icesjms/fsr041)Abstract/FREE Full TextGoogle Scholar
Garcia S. M., Rosenberg A. 2011 Food security and marine capture fisheries: characteristics, trends, drivers and future perspectives. Phil. Trans. R. Soc. B 365, 2869–2880. doi:10.1098/rstb.2010.0171 (doi:10.1098/rstb.2010.0171)CrossRefGoogle Scholar
Branch T., Jensen O. P., Ricard D., Ye Y., Hilborn R. 2011 Contrasting global trends in marine fishery status obtained from catches and from stock assessments. Conserv. Biol. 25, 777–786. doi:10.1111/j.1523-1739.2011.01687.x (doi:10.1111/j.1523-1739.2011.01687.x)CrossRefPubMedGoogle Scholar
Sarmiento J. L. 2004 Response of ocean ecosystems to climate warming. Glob. Biogeochem. Cycle 18, 3–23.Google Scholar
Brander K. M. 2007 Climate change and food security special feature: global fish production and climate change. Proc. Natl Acad. Sci. USA 104, 19 709–19 714. doi:10.1073/pnas.0702059104 (doi:10.1073/pnas.0702059104) Abstract/FREE Full TextGoogle Scholar
Jochum M., Schneider F. D., Crowe T. P., Brose U., O’Gorman E. J. 2012 Climate-induced changes in bottom-up and top-down processes independently alter a marine ecosystems. Phil. Trans. R. Soc. B 367, 2962–2970. doi:10.1098/rstb.2012.0237 (doi:10.1098/rstb.2012.0237)Abstract/FREE Full TextGoogle Scholar
Twomey M., Brodte E., Jacob U., Brose U., Crowe T. P., Emmerson M. C. 2012 Idiosyncratic species effects confound size-based predictions of responses to climate change. Phil. Trans. R. Soc. B 367, 2971–2978. doi:10.1098/rstb.2012.0244 (doi:10.1098/rstb.2012.0244)Abstract/FREE Full TextGoogle Scholar
Rall B. C., Brose U., Hartvig M., Kalinkat G., Schwarzmüller F., Vucic-Pestic O., Petchey O. L. 2012 Universal temperature and body-mass scaling of feeding rates. Phil. Trans. R. Soc. B 367, 2923–2934. doi:10.1098/rstb.2012.0242 (doi:10.1098/rstb.2012.0242)Abstract/FREE Full TextGoogle Scholar
Perry A. L., Low P. J., Ellis J. R., Reynolds J. D. 2005 Climate change and distribution shifts in marine fishes. Science 308, 1912–1915. doi:10.1126/science.1111322 (doi:10.1126/science.1111322)Abstract/FREE Full TextGoogle Scholar
Blanchard J. L., Mills C., Jennings S., Fox C. J., Rackham B. D., Eastwood P. D., O’Brien C. M. 2005 Distribution–abundance relationships for North Sea Atlantic cod (Gadus morhua): observation versus theory. Can. J. Fish. Aquat. Sci. 62, 2001–2009. doi:10.1139/f05-109 (doi:10.1139/f05-109)CrossRefWeb of ScienceGoogle Scholar
Simpson S. D., Jennings S., Johnson M. P., Blanchard J. L., Schön P.-J., Sims D. W., Genner M. 2011 Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Curr. Biol. 21, 1565–1570. doi:10.1016/j.cub.2011.08.016 (doi:10.1016/j.cub.2011.08.016)CrossRefPubMedGoogle Scholar
Woodward G., et al. 2010 Ecological networks in a changing climate. Adv. Ecol. Res. 42, 71–138. doi:10.1016/B978-0-12-381363-3.00002-2 (doi:10.1016/B978-0-12-381363-3.00002-2)CrossRefGoogle Scholar
Nogues-Bravo D., Rahbek C. 2011 Communities under climate change. Science 334, 1070–1071. doi:10.1126/science.1214833 (doi:10.1126/science.1214833)Abstract/FREE Full TextGoogle Scholar
Brose U., Dunne J. A., Montoya J. M., Petchey O. L., Schneider F. D., Jacob U. 2012 Climate change in size-structured ecosystems. Phil. Trans. R. Soc. B 367, 2903–2912. doi:10.1098/rstb.2012.0232 (doi:10.1098/rstb.2012.0232)Abstract/FREE Full TextGoogle Scholar
Travers M., Shin Y.-J., Jennings S., Machu E., Huggett J. A., Field J. G., Cury P. M.. 2009 Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela. Ecol. Model. 220, 3089–3099. doi:10.1016/j.ecolmodel.2009.08.016 (doi:10.1016/j.ecolmodel.2009.08.016)CrossRefWeb of ScienceGoogle Scholar
Marzloff M., Shin Y., Tam J., Travers M., Bertrand A. 2009 Trophic structure of the Peruvian marine ecosystem in 2000–2006: insights on the effects of management scenarios for the hake fishery using the IBM trophic model Osmose. J. Mar. Syst. 75, 290–304. doi:10.1016/j.jmarsys.2008.10.009 (doi:10.1016/j.jmarsys.2008.10.009)CrossRefWeb of ScienceGoogle Scholar
Cheung W. W. L., Lam V. W. Y., Sarmiento J. L., Kearney K., Watson R., Pauly D. 2009 Projecting global marine biodiversity impacts under climate change scenarios. Fish Fish. 10, 235–251. doi:10.1111/j.1467-2979.2008.00315.x (doi:10.1111/j.1467-2979.2008.00315.x)CrossRefGoogle Scholar
Cheung W. W. L., Lam V. W. Y., Sarmiento J. L., Kearney K., Watson R., Zeller D., Pauly D. 2010 Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob. Change Biol. 16, 24–35. doi:10.1111/j.1365-2486.2009.01995.x (doi:10.1111/j.1365-2486.2009.01995.x)CrossRefWeb of ScienceGoogle Scholar
Binzer A., Guill C., Brose U., Rall B. C. 2012 The dynamics of food chains under climate change and nutrient enrichment. Phil. Trans. R. Soc. B 367, 2935–2944. doi:10.1098/rstb.2012.0230 (doi:10.1098/rstb.2012.0230)Abstract/FREE Full TextGoogle Scholar
Jennings S., Brander K. 2010 Predicting the effects of climate change on marine communities and the consequences for fisheries. J. Mar. Syst. 79, 418–426. doi:10.1016/j.jmarsys.2008.12.016 (doi:10.1016/j.jmarsys.2008.12.016)CrossRefWeb of ScienceGoogle Scholar
Sheldon R. W., Sutcliffe W. H. Jr., Paranjape M. A. 1977 Structure of pelagic food chain and relationship between plankton and fish production. J. Fish. Res. Board Can. 34, 2344–2355. doi:10.1139/f77-314 (doi:10.1139/f77-314)CrossRefGoogle Scholar
Dickie L. M., Kerr S. R., Boudreau P. R. 1987 Size-dependent processes underlying regularities in ecosystem structure. Ecol. Monogr. 57, 233–250. doi:10.2307/2937082 (doi:10.2307/2937082)CrossRefWeb of ScienceGoogle Scholar
Jennings S., Mélin F., Blanchard J. L., Forster R. M., Dulvy N. K., Wilson R. W. 2008 Global-scale predictions of community and ecosystem properties from simple ecological theory. Proc. R. Soc. B 275, 1375–1383. doi:10.1098/rspb.2008.0192 (doi:10.1098/rspb.2008.0192)Abstract/FREE Full TextGoogle Scholar
Benoît E., Rochet M.-J. 2004 A continuous model of biomass size spectra governed by predation and the effects of fishing on them. J. Theor. Biol. 226, 9–21. doi:10.1016/S0022-5193(03)00290-X (doi:10.1016/S0022-5193(03)00290-X)CrossRefPubMedWeb of ScienceGoogle Scholar
Law R., Plank M. J., James A., Blanchard J. L. 2009 Size-spectra dynamics from stochastic predation and growth of individuals. Ecology 90, 802–811. doi:10.1890/07-1900.1 (doi:10.1890/07-1900.1)CrossRefPubMedWeb of ScienceGoogle Scholar
Maury O., Shin Y., Faugeras B., Benari T., Marsac F. 2007 Modeling environmental effects on the size-structured energy flow through marine ecosystems. II. Simulations. Prog. Oceanogr. 74, 500–514. doi:10.1016/j.pocean.2007.05.001 (doi:10.1016/j.pocean.2007.05.001)CrossRefWeb of ScienceGoogle Scholar
Castle M. D., Blanchard J. L., Jennings S. 2011 Predicted effects of behavioural movement and passive transport on individual growth and community size structure in marine ecosystems. Adv. Ecol. Res. 45, 41–66. doi:10.1016/B978-0-12-386475-8.00002-2 (doi:10.1016/B978-0-12-386475-8.00002-2)CrossRefGoogle Scholar
Blanchard J. L., Law R., Castle M. D., Jennings S. 2011 Coupled energy pathways and the resilience of size-structured food webs. Theor. Ecol. 4, 289–300. doi:10.1007/s12080-010-0078-9 (doi:10.1007/s12080-010-0078-9)CrossRefGoogle Scholar
Blanchard J. L., Jennings S., Law R., Castle M. D., McCloghrie P., Rochet M.-J., Benoit E.. 2009 How does abundance scale with body size in coupled size-structured food webs? J. Anim. Ecol. 78, 270–280. doi:10.1111/j.1365-2656.2008.01466.x (doi:10.1111/j.1365-2656.2008.01466.x)CrossRefPubMedWeb of ScienceGoogle Scholar
Shurin J. B., Clasen J. L., Greig H. S., Kratina P., Thompson P. L. 2012 Warming shifts top-down and bottom-up control of pond food web structure and function. Phil. Trans. R. Soc. B 367, 3008–3017. doi:10.1098/rstb.2012.0243 (doi:10.1098/rstb.2012.0243)Abstract/FREE Full TextGoogle Scholar
Holt J., et al. 2009 Modelling the global coastal ocean. Phil. Trans. R. Soc. A 367, 939–951. doi:10.1098/rsta.2008.0210 (doi:10.1098/rsta.2008.0210)Abstract/FREE Full TextGoogle Scholar
Holt J. T., James I. D. 2006 As assessment of the fine-scale eddies in a high-resolution model of the shelf seas west of Great Britain. Ocean Model. 13, 271–291. doi:10.1016/j.ocemod.2006.02.005 (doi:10.1016/j.ocemod.2006.02.005)CrossRefWeb of ScienceGoogle Scholar
Blackford J. C., Allen J. I., Gilbert F. J. 2004 Ecosystem dynamics at six contrasting sites: a generic modelling study. J. Mar. Syst. 52, 191–215. doi:10.1016/j.jmarsys.2004.02.004 (doi:10.1016/j.jmarsys.2004.02.004)CrossRefWeb of ScienceGoogle Scholar
Allen J. I., Holt J. T., Blackford J., Proctor R. 2004 Error quantification of a high-resolution coupled hydrodynamic-ecosystem coastal-ocean model. II. Chlorophyll-a, nutrients and SPM. J. Mar. Syst. 68, 381–404. doi:10.1016/j.jmarsys.2007.01.005 (doi:10.1016/j.jmarsys.2007.01.005)CrossRefGoogle Scholar
Holt J. T., Allen J. I., Proctor R., Gilbert F. 2005 Error quantification of a high-resolution coupled hydrodynamic-ecosystem coastal–ocean model: Part 1. Model overview and assessment of the hydrodynamics. J. Mar. Syst. 57, 167–188. doi:10.1016/j.jmarsys.2005.04.008 (doi:10.1016/j.jmarsys.2005.04.008)CrossRefWeb of ScienceGoogle Scholar
IPCC 2007 Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor & H. L. Miller). Cambridge, UK: Cambridge University Press.Google Scholar
Haines G. C. K. 2009 Evaluation of the S(T) assimilation method with the Argo dataset. Q. J. R. Meteorol. Soc. 135, 739–756. doi:10.1002/qj.395 (doi:10.1002/qj.395)CrossRefWeb of ScienceGoogle Scholar
Seitzinger S. P., Harrison J. A., Dumont E., Beusen A. H. W., Bouwman A. F. 2005 Sources and delivery of carbon, nitrogen and phosphorus to the coastal zone: an overview of the Global Nutrient Export from Watersheds (NEWS) models and their application. Glob. Biogeochem. Cycle 19, GB4S01. doi:10.1029/2005GB002606 (doi:10.1029/2005GB002606)CrossRefGoogle Scholar
Barnes C., Maxwell D., Reuman D. C., Jennings S. 2010 Global patterns in predator–prey size relationships reveal size dependency of trophic transfer efficiency. Ecology 91, 222–232. doi:10.1890/08-2061.1 (doi:10.1890/08-2061.1)CrossRefPubMedWeb of ScienceGoogle Scholar
Brown J., Gillooly J., Allen A., Savage V. 2004 Toward a metabolic theory of ecology. Ecology 85, 1771–1789. doi:10.1890/03-9000 (doi:10.1890/03-9000)CrossRefWeb of ScienceGoogle Scholar
Law R., Plank M., Kolding J. 2012 On balanced exploitation of marine 1 ecosystems: results from dynamic size spectra. ICES J. Mar. Sci. 69, 602–614. doi:10.1093/icesjms/fss031 (doi:10.1093/icesjms/fss031)Abstract/FREE Full TextGoogle Scholar
Pauly D. 1980 On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. J. Cons. Int. Explor. Mer. 39, 175–192. doi:10.1093/icesjms/39.2.175 (doi:10.1093/icesjms/39.2.175)CrossRefWeb of ScienceGoogle Scholar
Ware D. M., Thomson R. E. 2005 Bottom-up ecosystem trophic dynamics determine fish production in the Northeast Pacific. Science 308, 1280–1284. doi:10.1126/science.1109049 (doi:10.1126/science.1109049)Abstract/FREE Full TextGoogle Scholar
Chassot E., Bonhommeau S., Dulvy N. K., Mélin F., Watson R., Gascuel D., Le Pape O. 2010 Global marine primary production constrains fisheries catches. Ecol. Lett. 13, 495–505. doi:10.1111/j.1461-0248.2010.01443.x (doi:10.1111/j.1461-0248.2010.01443.x)CrossRefPubMedWeb of ScienceGoogle Scholar
Boudreau P. R., Dickie L. M. 1992 Biomass spectra of aquatic ecosystems in relation to fisheries yield. Can. J. Fish Aquat. Sci. 49, 1528–1538. doi:10.1139/f92-169 (doi:10.1139/f92-169)CrossRefWeb of ScienceGoogle Scholar
Jennings S., Blanchard J. L. 2004 Fish abundance with no fishing: predictions based on macroecological theory. J. Anim. Ecol. 73, 632–642. doi:10.1111/j.0021-8790.2004.00839.x (doi:10.1111/j.0021-8790.2004.00839.x)CrossRefWeb of ScienceGoogle Scholar
Petrie B., Frank K. T., Shackell N. L., Leggett W. C. 2009 Structure and stability in exploited marine fish communities: quantifying critical transitions. Fish Oceanogr. 18, 83–101. doi:10.1111/j.1365-2419.2009.00500.x (doi:10.1111/j.1365-2419.2009.00500.x)CrossRefWeb of ScienceGoogle Scholar
Longhurst A. 2007 Doubt and certainty in fishery science: are we really headed for a global collapse of stocks? Fish Res. 86, 1–5. doi:10.1016/j.fishres.2007.02.004 (doi:10.1016/j.fishres.2007.02.004)CrossRefWeb of ScienceGoogle Scholar
Perry R. I., Cury P., Brander K., Jennings S., Mollmann C., Planque B. 2010 Sensitivity of marine systems to climate and fishing: concepts, issues and management responses. J. Mar. Syst. 79, 427–435. doi:10.1016/j.jmarsys.2008.12.017 (doi:10.1016/j.jmarsys.2008.12.017)CrossRefWeb of ScienceGoogle Scholar
Hsieh C., Reiss C. S., Hunter J. R., Beddington J. R., May R. M., Sugihara G. 2006 Fishing elevates variability in the abundance of exploited species. Nature 443, 859–862. doi:10.1038/nature05232 (doi:10.1038/nature05232)CrossRefPubMedWeb of ScienceGoogle Scholar
Anderson C. N. K., Hsieh C., Sandin S. A., Hewitt R., Hollowed A., Beddington J., May R. M., Sugihara G. 2008 Why fishing magnifies fluctuations in fish abundance? Nature 452, 835–839. doi:10.1038/nature06851 (doi:10.1038/nature06851)CrossRefPubMedWeb of ScienceGoogle Scholar
Rochet M.-J., Benoît E. 2012 Fishing destabilizes the biomass flow in the marine size spectrum. Proc. R. Soc. B 279, 284–292. doi:10.1098/rspb.2011.0893 (doi:10.1098/rspb.2011.0893)Abstract/FREE Full TextGoogle Scholar
MacNeil M. A., Graham N. A. J., Cinner J. E., Dulvy N. K., Loring P. A., Jennings S., Polunin N. V. C., Fisk A. T., McClanahan T. R.. 2010 Transitional states in marine fisheries: adapting to predicted global change. Phil. Trans. R. Soc. B 365, 3753–3763. doi:10.1098/rstb.2010.0289 (doi:10.1098/rstb.2010.0289)Abstract/FREE Full TextGoogle Scholar
Branch T., Watson R., Fulton E. A., Jennings S., McGilliard C. R., Pablico G. T., Ricard D., Tracey S. R., 2010 The trophic fingerprint of marine fisheries. Nature 468, 431–435. doi:10.1038/nature09528 (doi:10.1038/nature09528)CrossRefPubMedWeb of ScienceGoogle Scholar
Baumgartner T., et al. 1999 Worldwide large-scale fluctuations of sardine and anchovy populations. S. Afr. J. Mar. Sci. 21, 289–347. doi:10.2989/025776199784125962 (doi:10.2989/025776199784125962)CrossRefWeb of ScienceGoogle Scholar
Burke L., Reytar K., Spalding M., Perry A. L. 2011 Reefs at risk revisited. Washington, DC: World Resources Institute.Google Scholar
Merino G., Barange M., Mullon C. 2010 Climate variability and change scenarios for a marine commodity: modelling small pelagic fish, fisheries and fishmeal in a globalized market. J. Mar. Syst. 81, 196–205. doi:10.1016/j.jmarsys.2009.12.010 (doi:10.1016/j.jmarsys.2009.12.010)CrossRefWeb of ScienceGoogle Scholar
Merino G., Barange M., Mullon C., Rodwell L. 2010 Impacts of global environmental change and aquaculture expansion on marine ecosystems. Glob. Environ. Change 20, 586–596. doi:10.1016/j.gloenvcha.2010.07.008 (doi:10.1016/j.gloenvcha.2010.07.008)CrossRefWeb of ScienceGoogle Scholar