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What Catch Data Can Tell Us About the Status of Global Fisheries Rainer

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What Catch Data Can Tell Us About the Status of Global Fisheries Rainer View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by OceanRep What catch data can tell us about the status of global fisheries Rainer Froese, Dirk Zeller, Kristin Kleisner & Daniel Pauly Marine Biology International Journal on Life in Oceans and Coastal Waters ISSN 0025-3162 Volume 159 Number 6 Mar Biol (2012) 159:1283-1292 DOI 10.1007/s00227-012-1909-6 1 23 Your article is protected by copyright and all rights are held exclusively by Springer- Verlag. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s version for posting to your own website or your institution’s repository. You may further deposit the accepted author’s version on a funder’s repository at a funder’s request, provided it is not made publicly available until 12 months after publication. 1 23 Author's personal copy Mar Biol (2012) 159:1283–1292 DOI 10.1007/s00227-012-1909-6 ORIGINAL PAPER What catch data can tell us about the status of global fisheries Rainer Froese • Dirk Zeller • Kristin Kleisner • Daniel Pauly Received: 11 January 2012 / Accepted: 23 February 2012 / Published online: 9 March 2012 Ó Springer-Verlag 2012 Abstract The only available data set on the catches of speech of a honorary doctorate from the University of global fisheries are the official landings reported annually Rhode Island (see Saila and Roedel 1980). He was serious: by the Food and Agriculture Organization of the United in most situations, it is the catch of commercial fishing Nations (FAO). Attempts to detect and interpret trends in vessels that constitutes the basis for estimating past and these data have been criticized as being both technically present biomass, and which then forms the basis for pro- and conceptually flawed. Here, we explore and refute these viding advice on next year’s catch. Obviously, catch cannot claims. We show explicitly that trends in catch data are not be taken from zero biomass, and in most commercial spe- an artifact of the applied method and are consistent with cies the annual catch cannot be larger than the average trends in biomass data of fully assessed stocks. We also annual biomass. In surplus production models (e.g., show that, while comprehensive stock assessments are the Schaefer 1954), catch relative to the maximum sustainable preferred method for evaluating single stocks, they are a yield (MSY) is a predictor for the relative biomasses that biased subsample of the stocks in a given area, strongly can support such catch in the long term, see Eq. 1. underestimating the percentage of collapsed stocks. We In previous publications, we (Froese and Kesner-Reyes concur with a recent assessment-based analysis by FAO 2002, 2009; Froese and Pauly 2003; Pauly et al. 2008; that the increasing trends in the percentage of overex- Zeller et al. 2009; Kleisner and Pauly 2011) and others ploited, depleted, and recovering stocks and the decreasing (Grainger and Garcia 1996; FAO 2010; Garibaldi 2012; trends in underexploited and moderately exploited stocks Worm et al. 2006, 2007) have analyzed global catch data to give cause for concern. We show that these trends are gain insights into the status of global fisheries, revealing for much more pronounced if all available data are considered. example an increase in collapsed stocks and a decline in new stocks. These attempts were criticized by Branch et al. (2011) and categorized as ‘‘both technically and concep- Introduction tually flawed’’ by Daan et al. (2011). Without presenting new data or insights into the status of global fisheries and ‘‘Fisheries managers need to know three things: the catch, without paying due attention to previous discussions of this the catch, and the catch,’’ John Gulland, then Chief of the topic (Worm et al. 2007), Branch et al. (2011) and Daan FAO’s Marine Resources Service quipped in his acceptance et al. (2011) conclude that reports about the critical status of world fisheries are exaggerated. Similarly, Carruthers et al. (2012) compare the methods of Froese and Kesner- Communicated by U. Sommer. Reyes (2002) and Kleisner and Pauly (2011) with surplus R. Froese (&) production assessments and find that the analyses based GEOMAR, Du¨sternbrooker Weg 20, 24105 Kiel, Germany only on catches provide fewer correct classifications than e-mail: [email protected] the more informed assessment models when applied to simulated data. Here, we address this criticism as follows: D. Zeller Á K. Kleisner Á D. Pauly Fisheries Centre, University of British Columbia, We show that (1) the maximum catch (Cmax) in a time 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada series is highly correlated with an internationally accepted 123 Author's personal copy 1284 Mar Biol (2012) 159:1283–1292 reference point, namely the maximum sustainable yield landings and biomass data of 50 fully assessed stocks of (MSY); (2) the trends visible in global catch data are not an the Northeast Atlantic as provided by ICES (http://www. artifact of the employed algorithm, as has been proposed ices.dk) and respective reference points (MSY and the by Wilberg and Miller (2007), Branch et al. (2011), and corresponding biomass Bmsy) as estimated by Froese and Daan et al. (2011); (3) the biomass trends for fully assessed Proelß (2010). stocks in the Northeast Atlantic are consistent with the For the simulations of random catch data, we followed trends derived from catch data analysis of these stocks; (4) the approach used by Daan et al. (2011) and created 1,953 only few stocks are rebuilding globally and in the North- time series (same number as nominal FAO stocks) under east Atlantic; and (5) analyses based only on assessed the condition of a uniform distribution of random numbers stocks strongly underestimate the number of collapsed between 0 and 1. stocks. We then present some improvements to the analysis An Excel spreadsheet (193 MB) with the analysis of the of catch data and conclude with an updated analysis of the FAO data is available for download at http://www.fishbase. status of global fisheries. de/rfroese/FAO_Catch_2009_MarBio.xls. The comparison between the 1950–1999 and 1950–2009 data sets is available under http://www.fishbase.de/rfroese/FAO_Catch_ Materials and methods 2009_MarBio99_3.xls. The analysis of fully assessed stocks of the Northeast Atlantic is available under http:// We used the original classification of stocks into categories www.fishbase.de/rfroese/BiomassMarBio.xls. of exploitation, based on catch relative to the maximum catch (Cmax) of Froese and Kesner-Reyes (2002) (Table 1). We refer to this classification as the original algorithm. Results Data on global capture production for 1950–2009 were obtained from http://www.fao.org in May 2011. These are Maximum catch (Cmax) is highly correlated landings data reported to and harmonized by FAO (Gari- with maximum sustainable yield (MSY) baldi 2012). For convenience, we refer to them in this study as FAO catch data. ISSCAAP groups of marine species Froese and Kesner-Reyes (2002) propose that catches were selected as in Froese and Kesner-Reyes (2002, 2009). between 0.5 and 1.0 Cmax are indicative of fully exploited FAO species items per marine FAO area were treated as stocks. Thus, they implicitly assume that the maximum nominal stocks. Altogether 1,953 stocks with more than sustainable yield (MSY) would normally be found within 10,000 t accumulated landings in their respective time this range. That assumption is confirmed by the linear series were included in the analyses. relationship between log Cmax and log MSY shown in We introduced a new category rebuilding for the years Fig. 1, where 98% of the variability in Cmax is accounted in which a stock recovered from the collapsed status for by MSY, for 50 fully assessed stocks in the Northeast (C \ 0.1 Cmax) to the fully exploited status (C [ 0.5 Cmax) Atlantic (Froese and Proelß 2010). A similar relationship (Table 2). was found by Srinivasan et al. (2010) for stocks from the Some categories require information from a preceding or Northwest Atlantic. Also, the median MSY/Cmax ratio in subsequent year, which is obviously not available in the first the 50 Northeast Atlantic stocks in Fig. 1 was 0.62 (95% and last year of a data series, respectively. We discuss these confidence limits, 0.56–0.70), that is, well within the range issues in the section Dealing with boundary effects. proposed by Froese and Kesner-Reyes (2002). In summary, For comparison of maximum catch with MSY and of it seems justified to assume that in a majority of fisheries, catch-based analysis with biomass-based analysis, we used catch levels of 0.5–1.0 Cmax are indicative of fully exploited stocks. Table 1 Original criteria used by Froese and Kesner-Reyes (2002) Trends in global catch data are not artifacts for assigning exploitation stages to fisheries, based only on catch data of the applied algorithm (C) relative to maximum catch (Cmax) Status of fishery Year C/Cmax In the following, we examine the suggested technical flaws of the original algorithm for predicting stock status from Undeveloped/No info Before C = Cmax \0.1 time series of catches relative to the historical C .In Developing 0.1–0.5 max surplus production models, catch is a predictor of two Fully exploited Before or after C = C [0.5 max equilibrium biomasses: either above or below the biomass Overexploited After C = C 0.1–0.5 max that can produce the maximum sustainable yield (B ) Collapsed \0.1 msy (Eq. 1). 123 Author's personal copy Mar Biol (2012) 159:1283–1292 1285 Table 2 Updated criteria used for assigning exploitation stages based to the undeveloped/no info category because lack of biomass on catches (C) relative to maximum catch (Cmax), catches relative to estimates represents lack of assessment but not necessarily lack of MSY, and biomass B relative to Bmsy.
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