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Use of genetic data to assess the uncertainty in stock assessments due to the assumed stock structure: the case of albacore from the Atlantic Ocean
Fishery Bulletin, Jan, 2007 by Haritz Arrizabalaga, Victoria Lopez-Rodas, Eduardo Costas, Alberto Gonzalez-Garces
Stock assessments can be problematic because of uncertainties associated with the data or because of simplified assumptions made when modeling biological processes (Rosenberg and Restrepo, 1995). For example, the common assumption in stock assessments that stocks are homogeneous and discrete (i.e., there is no migration between the stocks) is not necessarily true (Kell et al., 2004a, 2004b).
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On the other hand, it is essential that the stock structure assumed during the assessment and management process corresponds to the real population structure of the resource. Otherwise, fishery management becomes inefficient (less productive populations may be overfished and collapse, while more productive populations may be underexploited [Allendorf et al., 1987; Begg et al., 1999]) and may affect biological attributes, such as growth, productivity, or genetic diversity (Ricker, 1981). In spite of this problem, current regulations on several fisheries are based on spatial schemes that do not necessarily reflect the real biological structure of the populations (Pawson and Jennings, 1996; Stephenson, 1999; Ward, 2000). In these cases, the results of stock assessments may be biased and, in general, an important level of uncertainty exists in stock assessments (NRC, 1994; Turner, 1998) due to the assumed stock structure.
An assessment of the magnitude of this uncertainty is important so as to increase confidence in the assessment itself. Moreover, quantifying the uncertainty allows the evaluation of the relative effect of stock structure assumptions with respect to other assumptions about biological, fishery, or modeling parameters in the assessment. Knowing the relative importance of the effect of these underlying assumptions will allow management scientists to prioritize the types of research needed to better ground the stock assessments with real information.
In this note, we suggest a way to assess uncertainty in stock assessments that is due to assumptions of stock structure. The assessment is essentially based on a sensitivity analysis conducted by testing alternative stock structure hypotheses derived from available genetic, fishery, and biological information. The method is illustrated with albacore (Thunnus alalunga, Bonn. 1788) in the Atlantic Ocean.
Albacore is a highly migratory species distributed between latitudes 45[degrees]N and 45[degrees]S. Studies of albacore reproduction in the Atlantic Ocean have shown different spawning periods and areas in both hemispheres (Beardsley, 1969; Koto, 1969). Shiohama (1971) and Uozumi (1996), based on Japanese longline distribution studies, described an adult concentration area in each hemisphere. These findings, along with studies of larval concentration areas (Ueyanagi, 1971), support the existence of two separate populations, one in each hemisphere. Based on these studies, it is assumed within the International Commission for the Conservation of Atlantic Tunas (ICCAT) that there are two albacore management units in the Atlantic, separated by parallel 5[degrees]N. However, various authors have suggested the possibility that albacore move between the north and south Atlantic (reviewed in Gonzalez-Garces, 1997). Moreover, the continuous spatial distribution of catches around the equator also suggests this possibility (Fig. 1).
[FIGURE 1 OMITTED]
Recent studies have shown genetic differences between north and south Atlantic albacore (Takagi et al., 2001; Arrizabalaga et al., 2004), but it is still unclear whether the limit between both populations is at latitude 5[degrees]N or somewhere else. In fact, results from Arrizabalaga et al. (2004) are not concordant with the limit at latitude 5[degrees]N because a sample from the Gulf of Guinea (I[degrees]N, 15-16[degrees]W) was genetically more like the sample from the north Atlantic than the one from the south Atlantic. This observation may indicate that either the limit between both stocks may be located farther south than that currently assumed or that there may be some interchange between individuals of both stocks. An earlier statistical comparison of blood group frequencies in albacore found in the Gulf of Guinea (lat. 0[degrees]-9[degrees]S, long. 0[degrees]-8[degrees]W), northwest Atlantic (lat. 23[degrees]-31[degrees]N, long. 60[degrees]-70[degrees]W) and middle-north Atlantic (lat. 1-34[degrees]N, long. 11[degrees]-40[degrees]W) in an earlier study (Suzuki, 1962) did not show differences between them, again indicating that the fish present in the Gulf of Guinea may belong to the northern population.
Materials and methods
Taking into account the above findings, we assessed the uncertainty in north and south Atlantic albacore stock assessments by means of a sensitivity analysis. This analysis consisted in assessing both stocks, either under alternative stock boundaries or by assuming certain migration rates between them.
Stock assessment under the assumption of alternative boundaries between stocks
Two alternative boundaries between albacore stocks were considered: at lat. 0[degrees]N and lat. 5[degrees]S. The catch-at-age within lat. 5[degrees]N-0[degrees]N and lat. 5[degrees]N-5[degrees]S was removed from the southern catch-at-age matrix and added to the northern one, by using available catch (ICCAT (1)), size, and growth information (Bard, 1981; Sarralde et al., 2002). For each boundary, abundance and fishing mortality rates were estimated separately for each stock by virtual population analysis (VPA) by using the VPA-2box, vers. 3.0 program (Porch et al., 2001). This program assesses the abundance and mortality of one or two intermixing stocks by fitting age-structured population equations to fishery data. All stock assessment options were maintained as in the ICCAT 2001 report (ICCAT (2)) and variance of estimated parameters was computed by performing 400 nonparametric bootstraps of the abundance indices.
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