BS Halpern

C Costello

Daniel Pauly

Dirk Zeller

EK Pikitch

K Kleisner

Kristin Kleisner

N Daan

R Froese

Rainer Froese

UT Srinivasan

Froese, Rainer

Zeller, Dirk

Kleisner, Kristin

Pauly, Daniel

OceanRep

COMMENT AND REPLYWorrisome trends in global stock status continue unabated:a response to a comment by R.M. Cook on ‘‘What catch datacan tell us about the status of global fisheries’’Rainer Froese • Dirk Zeller • Kristin Kleisner •Daniel PaulyReceived: 22 January 2013 / Accepted: 24 January 2013 / Published online: 6 February 2013 Springer-Verlag Berlin Heidelberg 2013In a previous contribution to this journal (Froese et al.2012), we refuted criticism of a simple method (Froese andKesner-Reyes 2002) that derives information about thestatus of global stocks from global catch data. This methodassumes that, for a given stock, the ratio of current catchesto previous maximum catches (Cmax) is indicative of thelikely current exploitation status of the stock. For example,the method considers a stock as ‘‘collapsed’’ if currentcatch is \10 % of the previous maximum catch.The method also assumes that current catches in therange of 0.5–1.0 Cmax are indicative of fully exploitedstocks, implying that the maximum sustainable yield(MSY) would also fall into that range. This assumption wassupported by the observation (Froese et al. 2012) that themedian MSY/Cmax ratio in 50 fully assessed stocks of theNortheast Atlantic was 0.62 (95 % confidence limits0.56–0.70). Also, a plot of log(Cmax) over log(MSY) forthese stocks showed a high correlation with little variancearound the regression line. Such correlation has also beenfound by other studies for other stocks (Srinivasan et al.2010; Halpern et al. 2012). Thus, in our previous paper, weconcluded that ‘‘it seems justified to assume that in amajority of fisheries, catch levels of 0.5–1.0 Cmax areindicative of fully exploited stocks’’ (Froese et al. 2012).A comment by Cook (2013) challenges this assumption,asserting that ‘‘Unfortunately, these analyses do notsupport their contention that MSY for a particular stock isrelated to maximum catch in a predictable way’’ Insupport of this statement, Cook (2013) points out that the95 % range of MSY/Cmax ratios for the 50 analyzed stocksspans from 0.34 to 1.19, thus exceeding the assumed rangeof 0.5–1.0. However, given that we expected our method tomake correct classifications not for 95 % but only for amajority of stocks, the fact that our range is located at thevery center of the wider 95 % range does not contradict,but rather supports our assumption. Also, Figure 1d inCook (2013), which presents the frequency distribution ofMSY/Cmax ratios, shows sharp drop-offs in frequencybelow 0.5 and above 1.0, further confirming that the rangewe selected is reasonable.Cook (2013) also criticizes our regression of log(Cmax)over log(MSY), pointing out that such a relationship wastrivial, because ‘‘It is obvious that small stocks will have alow MSY and large stocks will have a high MSY.’’Because of this scale effect, ‘‘[…] any random catch […] ishighly correlated with MSY when examined across stocksof widely differing magnitude.’’ We agree with this point,because it leads to the logical conclusion that the maximumcatch that can be taken from a stock is related to its size.However, if we assume that the maximum catches thatfisheries can take in the real world are approximated by thereported maximum catches of stocks that are exploitedsufficiently to be included in global statistics, then it alsofollows that these observed maximum catches (Cmax) arerelated to their respective stock sizes and their corre-sponding MSY values, a point that was disputed by ourcritics (e.g., Daan et al. 2011). This inference is confirmedby Figure 1a in Cook (2013), which shows regressions ofmaximum and random catch over MSY, on log-scales.Consistent with the above reasoning, the regression linerepresenting Cmax lies above the regression line withCommunicated by U. Sommer.R. Froese (&)GEOMAR, Du¨sternbrooker Weg 20, 24105 Kiel, Germanye-mail: rfroese@geomar.deD. Zeller  K. Kleisner  D. PaulyFisheries Centre, University of British Columbia,2202 Main Mall, Vancouver, BC V6T 1Z4, Canada123Mar Biol (2013) 160:2531–2533DOI 10.1007/s00227-013-2185-9random data, and it accounts for more of the variability inthe data. Note that Cook (2013) in his Figure 1a does notpresent a random or median run of random data, but ratherthe run that produced the highest r2 value. Thus, the truedifference between Cmax and random catch data would belarger than that shown in his Figure 1a.We agree with Cook (2013) that plotting of log-trans-formed data visually reduces the existing variability andthat, if scale effects occur, the coefficient of determinationin a regression analysis overrepresents the variation inY that is accounted for by X. Thus, we reproduce hereFigure 1 of Froese et al. (2012), replacing the regressionline with lines representing 0.5 and 1.0 ratios betweenMSY and Cmax, respectively (here also Fig. 1). In thispresentation, it becomes clear that over a wide range ofmaximum catches, most MSY estimates fall between 0.5and 1.0 Cmax.Figure 1c of Cook (2013) shows a cloud of points ofnormalized maximum catches plotted over normalizedMSY values. Cook (2013) uses this figure to argue that‘‘there is no relationship between Cmax and MSY.’’ How-ever, if this figure is augmented by diagonal lines repre-senting the 0.5–1.0 Cmax range, then it becomes clear that amajority of about 80 % of the points falls into that rangeand thus shows the behavior assumed by our method.In conclusion, we thank Cook (2013) for pointing outthat the coefficient of determination between log-trans-formed data of MSY and Cmax overrepresents their corre-lation, due to effects of scale stemming from very differentstock sizes. However, MSY and Cmax are related, despiteCook’s (2013) assertions to the contrary; indeed, MSY willtypically fall within the range of 0.5–1.0 Cmax, as originallystated by Froese and Kesner-Reyes (2002), and as illus-trated by Cook’s (2013) own analysis.We use this opportunity to point out that the status andtrends in global fish stocks as presented by Froese et al.(2012), based on the method discussed here, have beenconfirmed by subsequent publications using similar(Kleisner et al. 2012) and different methods (Costello et al.2012; Pikitch 2012). Froese and Kesner-Reyes (2002) werethe first to point out the worrisome high proportion andcontinuing increase in collapsed stocks, which in theiranalysis stood at 19 % in 1998. Here, we show the sub-sequent development, using the latest official data down-loaded from www.fao.org in January 2013 (Fig. 2). As canbe seen, the increase in collapsed stocks continues una-bated, reaching 24 % in 2010 and confirming thatrebuilding efforts are still insufficient on a global scale(FAO 2012).Acknowledgments We thank Josephine R. Barile for help with theanalysis of the latest FAO data. This is FIN contribution number 140.ReferencesCook RM (2013) A comment on ‘‘What catch data can tell us aboutthe status of global fisheries’’ (Froese et al. 2012). (2013) MarineBiologyCostello C, Ovando D, Hilborn R, Gaines SD, Deschenes O, LesterSE (2012) Status and solutions for the World’s unassessedfisheries. Science 338:517–520Daan N, Gislason H, Pope JG, Rice JC (2011) Apocalypse in worldfisheries? The reports of their death are greatly exaggerated.ICES J Marine Sci 68:1375–1378FAO (2012) State of the World Fisheries and Aquaculture 2012.FAO, RomeFroese R, Kesner-Reyes K (2002) Impact of fishing on the abundanceof marine species. ICES Document CM 2002/L:12, 15 ppFroese R, Zeller D, Kleisner K, Pauly D (2012) What catch datacan tell us about the status of global fisheries. Mar Biol 159:1283–1292Halpern BS, Longo C, Hardy D, McLeod KL, Samhouri JF, KatonaSK, Kleisner K, Lester SE, O’Leary J, Ranelletti M, RosenbergFig. 1 Plot of maximum sustainable yield (MSY) over maximumreported catch (Cmax) for 50 stocks in the Northeast Atlantic. Notethat most points fall between the lines representing 0.5–1.0 Cmax.[File: BiomasMarBio2.xls]Fig. 2 Percentage of global stocks that produce \10 % of previouscatches [File: FAO2010.xls]2532 Mar Biol (2013) 160:2531–2533123AA, Scarborough C, Selig ER, Best BD, Brumbaugh DR, ChapinFS, Crowder LB, Daly KL, Doney SC, Elfes C, Fogarty MJ,Gaines SD, Jacobsen KI, Karrer LB, Leslie HM, Neeley E, PaulyD, Polasky S, Ris B, Martin KS, Stone GS, Sumaila UR, Zeller D(2012) An index to assess the health and benefits of the globalocean. Nature 488:615–620Kleisner K, Zeller D, Froese R, Pauly D (2012) Using global catchdata for inferences on the world’s marine fisheries. Fish Fish.doi:10.1111/j.1467-2979.2012.00469.xPikitch EK (2012) The risks of overfishing. Science 338:474–475Srinivasan UT, Cheung WWL, Watson R, Sumaila UR (2010) Foodsecurity implications of global marine catch losses due tooverfishing. J Bioecon 12(3):183–200Mar Biol (2013) 160:2531–2533 2533123

Worrisome trends in global stock status continue unabated: a response to a comment by R.M. Cook on "What catch data can tell us about the status of global fisheries"

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Worrisome trends in global stock status continue unabated: a response to a comment by R.M. Cook on “What catch data can tell us about the status of global fisheries”

Worrisome trends in global stock status continue unabated: a response to a comment by R.M. Cook on "What catch data can tell us about the status of global fisheries"

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