Acoustic and egg-production estimates of South African anchovy biomass over a decade: comparisons, accuracy, and utility

Transcription

1 ICES Journal of Marine Science, 53: Acoustic and egg-production estimates of South African anchovy biomass over a decade: comparisons, accuracy, and utility Ian Hampton Hampton, I Acoustic and egg-production estimates of South African anchovy biomass over a decade: comparisons, accuracy, and utility. ICES Journal of Marine Science, 53: The spawning biomass of South African anchovy (Engraulis capensis) has been estimated acoustically and by the daily egg-production method every year since Recruit biomass has been estimated acoustically every year since The estimates are evaluated, chiefly through comparisons between the acoustic and egg-production estimates. The mean of the acoustic spawner biomass estimates, obtained using a recently developed target-strength expression, agreed to within 10% of the eggproduction mean, supporting the expression. The results are consistent with those from a population dynamics model which indicated relatively little bias in the acoustic estimates of spawner biomass compared to the corresponding recruit estimates, which the model indicated to be substantially negatively biased. The coefficient of variation in estimates of spawner biomass, obtained by scaling the current acoustic estimate by the average ratio between previous egg production and acoustic estimates, was typically 21%. It is considered that the value of further egg-production estimates in estimating anchovy biomass is diminishing, and that effort should be concentrated on improving the accuracy of direct acoustic estimates. Ways in which the different estimates have been used to manage the fishery over the past 10 years are briefly described and their utility discussed. It is concluded that the survey results have reliably reflected the major changes in anchovy recruit and spawner biomass that have occurred between 1984 and 1994, and have provided valuable information for managing the fishery over this period International Council for the Exploration of the Sea Key words: acoustics, anchovy, egg production, Engraulis capensis. I. Hampton: Sea Fisheries Research Institute, Private Bag X2, Roggebaai, Cape Town 8012, South Africa [tel: , fax: ]. Introduction Anchovy (Engraulis capensis (Gilchrist)) is an important commercial species on the South African continental shelf, annual catches having ranged between and t over the past three decades. Combined acoustic and egg-production surveys of spawner biomass have been made each November since 1984 (Hampton, 1987, 1992; Armstrong et al., 1988; Shelton et al., 1993), while acoustic surveys of recruitment have been conducted annually in mid-year since 1985 (Hampton, 1987, 1992). The time series of estimates is now considered to be long enough for general conclusions to be drawn concerning their validity and accuracy. This is attempted here by comparing the acoustic and egg-production estimates in different ways, with the aim of quantifying the overall bias in the acoustic estimates of both spawner and recruit biomass. The use and utility of the estimates in the management of the fishery over the past decade is also discussed briefly. Methods The acoustic surveys were run from the Sea Fisheries Research Institute s vessel, RV Africana according to standard echo integration practice (e.g. MacLennan and Simmonds, 1992) using a sphere-calibrated SIMRAD EKS-38 sounder up until 1990, and a 38 khz SIMRAD EK400 sounder thereafter, and locally developed digital echo-integrators. Further methodological details may be found in Hampton (1987 and 1992). The spawning surveys took place mainly on the South Coast of South Africa in November, and on the West Coast in mid-year during the height of the anchovy recruit fishery there. In all, 11 spawning surveys (1984 to 1994) and 10 recruit surveys (1985 to 1994) are considered here. In all of the spawning surveys except the most /96/ $18.00/ International Council for the Exploration of the Sea

2 494 I. Hampton S Hondeklipbaai Doringbaai Cape Columbine Mossel Bay DENSITY (t km 2 ) > E Port Elizabeth Cape St Francis Plettenberg Bay Cape Agulhas Figure 1. Distribution of anchovy spawners in November 1994, derived from acoustic data.

3 Acoustic and egg-production estimates of South African anchovy biomass 495 recent (1994), the biomass was also estimated by the daily egg-production method (Parker, 1980), using a CALVET net to collect eggs, and samples from the midwater trawl to estimate the necessary spawner parameters. Sampling variance was estimated using algorithms given in Armstrong et al. (1988). From 1985, the November surveys were carried out on random stratified grids of parallel transects (e.g. Fig. 1), usually in two phases. This design was introduced for the recruit surveys in Jolly and Hampton (1990) describe the survey design and the algorithms for estimating biomass and sampling variance. Corrections were made to all the acoustic estimates for fish too close inshore to be surveyed, which was a problem in the recruit surveys in particular. The correction, which was typically between 10 and 20%, was taken as the mean density on inshore transits between the transects, multiplied by half the inaccessible inshore area for reasons given in Hampton (1987). Target strengths were estimated from Halldórsson and Reynisson s (1983) expression for the weightnormalized target strength (TS kg ) of herring at 38 khz, viz: TS kg = 10.9 Log L t 20.9, (1) where L t is the total length in centimetres. This expression was used for all surveys, despite doubt about its applicability to anchovy, and a subsequent correction to it by Reynisson (1993), to maintain a consistent time series from which to develop a management procedure for the resource. In recent years, direct in situ estimates of E. capensis target strength have been made by Barange et al. (in press) from split-beam data, using various empirical criteria to screen out echoes accepted from multiple targets. Compared to Equation (1), their expression TS kg = 12.1 Log L t 21.1 (2) gives a difference of 1.3 db kg 1 for L t =8.0 cm (typical of recruits) and of 1.5 db kg 1 for L t =12 cm (typical of spawners). Results Figures 1 and 2 show maps of anchovy spawner and recruit distribution, respectively, from the two 1994 surveys, which are typical for the time series. The concentration of recruits inshore on the West Coast, and of adults on the South Coast west of Cape Agulhas, is evident. Table 1 sets out the egg-production (Ê i ) and acoustic estimates of spawner biomass, obtained through both Equations (1) and (2) ( i and  i, respectively), together with the corresponding coefficients of sampling variation in Ê i and  i (CV(Ê) i and CV(Â) i, respectively). The correction applied to  i to obtain  i was that for a typical spawner length of 12 cm. This simplified procedure was considered adequate considering the weak dependence of the correction on length. Also shown in Table 1 are values of R i and R i, the ratios of Ê i to  i and  i, respectively, and estimates of Var(R i ), obtained from the estimated sampling variances in  i and Ê i through Equation (4) (see Discussion). In Table 2, values of  i,â i, and CV(Â) i are shown for the recruit surveys. In this case the target-strength correction was that for a typical recruit length of 8.0 cm. The time series of  i,â i, and Ê i for the spawner surveys are shown in Figure 3a, and of  i and  i for the recruit surveys in Figure 3b. The pronounced minimum in both recruit and spawner biomass in 1989 and 1990, and their decline in recent years, will be noted. Discussion Error analysis The absolute accuracy of the acoustic surveys of spawner biomass can be assessed by comparing them with the corresponding egg-production estimates, assuming the latter to be unbiased. Figure 3a shows that the two time series track each other well, adding confidence in both. Furthermore, the R i values in Table 1 are generally closer to unity than the R i values (mean 0.90 vs. 1.29), which supports Equation (2), assuming that the net effect of all other biases was relatively small. The agreement also suggests that for the recruit surveys, the A i estimates in Table 2 are probably more accurate than the A i estimates. An attempt was made to determine whether any net bias in the acoustic estimates was constant and proportional by comparing Var(R i ), the observed variance in R i, with that expected from the variances in the egg and acoustic estimates for each of the surveys. If Var(R) is the variance of R, Var(R i )=Var(R î )+Var(R), (3) where Var(R î ) is the variance in the estimate of R i due to variance in the egg-production and acoustic estimates for year i, which can be estimated from: Var(R î )=[Var(Ê i )+R i 2 Var( i )]/ i 2. (4) It is assumed that the covariance between Ê i and  i is negligible, which appears reasonable from covariance estimates in Hampton et al. (1990). The data in Table 2 give Var(R i )=0.27. Substituting this in Equation (3), and replacing Var(R î ) by its mean for the 10 surveys (0.36) gives a negative value ( 0.09) for Var(R), indicating that the variances in  i and Ê i, and the

4 496 I. Hampton S DENSITY (t km 2 ) >100 Port Nolloth Hondeklipbaai Lambertsbaai CAPE TOWN Figure 2. Distribution of anchovy recruits in May 1994, derived from acoustic data. E uncertainty in estimating them, is too great in relation to Var(R) to estimate the latter. All that can be concluded is that the variability in the bias was probably small compared to the measurement error. The (assumed) unbiased egg-production estimates enable an absolute acoustic estimate of spawner biomass to be made from a relative acoustic estimate, and its variance estimated, without a knowledge of target strength on the magnitude of individual biases. For year i, the estimate, (Â abs ) i, is given by: (Â abs ) i =Rz (Â rel ) i, (5)

5 Acoustic and egg-production estimates of South African anchovy biomass 497 Table 1. Egg production (Ê i ) and acoustic estimates of spawner biomass obtained from Equations (1) ( i ) and (2) ( i ) in million tonnes. Also shown are the CVs in Ê i and  i, the ratios of Ê i to  i and  i (R i and R i ), and the variances in R i, estimated from Equation (4). Year Ê i CV(Ê) i  i CV(Â) i  i R i Var (R i ) R i Means where ( rel ) i is the relative acoustic estimate for year i, and Rz is the arithmetic mean of Ê/( rel ) for all previous years for which there are estimates. Var( abs ) i can be estimated from: Var( abs ) i =Rz 2 Var( rel ) i +( rel ) i 2 Var(R 0 )+ ( rel ) i 2 Var(R). (6) The first term estimates the variance due to sampling error in ( rel ) i, the second that due to uncertainty in the estimation of Rz, and the third the contribution from the variance in Rz itself. Treating the A i estimates in Table 1 as relative, and taking R as 1.29, gives 1.38 million t as the mean of ( abs ) i for the 10 joint surveys conducted to date. Taking Var(R 0 ) as (i.e., Var(R i )/10) and Var(R) as zero (for lack of evidence to the contrary), and substituting into Equation (6), with ( rel ) i and Var( rel ) i replaced by their means (1.07 million t and million t 2 respectively), gives 0.21 as a typical CV to be expected Table 2. Acoustic estimates of recruit biomass between 1985 and 1994, obtained using Equations (1) ( i ) and (2) ( i ). Also shown are the CVs in ( i ), and (A corr ) i estimates, obtained by correcting ( i ) for the bias estimated by Butterworth et al. (1993). Year  i  i (t 10 3 ) CV (Â) i (t 10 3 ) ( corr ) i (t 10 3 ) Means in estimates of (A abs ) i. The relative contributions to the variance from the first two terms in Equation (6) were 65% and 35%, respectively. Note that because of the relatively large number of existing survey estimates, the addition of further egg and acoustic estimates of similar precision to those conducted to date is unlikely to affect the estimate of Rz materially, or to reduce Var(R 0 ) significantly, until many more years data have been collected. Since the second term in any event contributes only some 35% to the total variance in ( abs ) i, the most effective way to increase precision would be by reducing Var( rel ) i in the current year. The recruit and spawner biomass estimates can also be evaluated through an examination of their consistency with one another, based on population dynamics models. The most recent study of this nature has been carried out by Butterworth et al. (1993), who used a maximum-likelihood approach to fit an earlier (Bergh and Butterworth, 1987) anchovy population model to A i and E i estimates from 1984 to (The A i estimates used were those in Table 1, but the E i estimates from 1988 to 1991 were about 20% higher than those in Table 1, due to a sampling bias which at that stage had not been corrected for.) The egg-production estimates were treated as absolute (i.e. as unbiased), and the acoustic estimates as relative. The acoustic biases were assumed to be constant and proportional in the spawner surveys, and variable and proportional in the recruit surveys. The results indicated that the acoustic surveys have underestimated the spawner biomass by some 15% on average, as opposed to the direct comparison between the acoustic and egg-production estimates, which indicates underestimation by about 25% for the data set used by Butterworth et al. (1993). The model-based estimates of the bias in the recruit surveys were far greater, indicating underestimation by 43% on average, equivalent to a bias factor of A i estimates corrected by this factor, (A corr ) i, are shown in

6 498 I. Hampton 2.0 Figure 3. Time series of acoustic estimates of anchovy spawner biomass (a) and recruitment (b), obtained using Equations (1) and (2) (solid and dashed lines respectively). Also shown in (a) are the corresponding egg-production estimates of spawner biomass (dotted lines). Error bars calculated from CVs. Biomass (million t) Figure 4. Trends in acoustic estimates of anchovy spawner biomass (solid line) compared to the proportion by mass of anchovy in the diet of Cape gannets (Morus capensis) on the West Coast (dotted line). Diet curve updated from data in Crawford and Dyer (1995), scaled to acoustic data. Table 2. The CV of the bias probability distribution was estimated at 18%. It is possible that the bias was largely due to behavioural factors specific to the recruit surveys, and in particular to the correction for fish too close to the coast to be assessed. As previously mentioned, this component can be large and variable, and since the correction for it is somewhat arbitrary, the results after correction could well contain a large variable negative bias, such as that revealed by Butterworth et al. (1993). Alternatively, the fact that anchovy in the recruit surveys were generally closer to the surface than in the spawner surveys (Sea Fisheries Research Institute, unpubl. data) could well have resulted in a significant and variable negative bias not present in the spawner surveys. Finally, additional support for the validity of the major fluctuations in biomass indicated in Figures 3a and 3b is given by synchronous changes in the feeding and breeding responses of seabirds in the area (Laugksch and Adams, 1993; Crawford and Dyer, 1995). The relationships were all most pronounced for the years (1989 and 1990), when the acoustic surveys indicated that both recruitment and spawner biomass were abnormally low. An example is given in Figure 4 (updated from data in Crawford and Dyer, 1995), which shows extremely close correspondence between the acoustic estimates of spawner biomass and the percentage by mass of anchovy in the diet of Cape gannets (Morus capensis) on the West Coast over the duration of the survey programme. Utility for management Over the past decade, management of the anchovy resource has been almost exclusively based on the annual acoustic and egg-production estimates of spawner biomass and the acoustic estimates of recruitment strength. The procedures used up until 1991 are summarized in Hampton (1992). In 1991, a new procedure was adopted in which the probability density functions of population dynamics parameters obtained from the earlier analysis were used in a Monte Carlo simulation to predict the effect of various fishing strategies and levels on the stock (Butterworth and Bergh, 1993). From this, a simple catch-control law was developed, aimed at removing a constant proportion of the stock each year. The only survey inputs in the catch-control law were the acoustic estimate of recruit numbers in the current year and of the number of spawners in the previous November, weighted according to expected growth and natural mortality between November and the time of the recruit survey. The egg-production estimates were no longer used directly. The procedure has recently been refined using a maximum likelihood method to estimate population parameters and survey biases (Butterworth et al., 1993), giving inter alia the bias estimates previously discussed. The new catch-control law is similar to the previous one, except that acoustic estimates of recruit numbers and spawner biomass are normalized by their means for previous years (thus accounting for constant multiplicative biases), and are weighted according to an optimization procedure, which for all data up to 1993 gives equal weight to the two estimates (De Oliveira, 1995). As new survey estimates are acquired, the current estimates

7 Acoustic and egg-production estimates of South African anchovy biomass 499 of bias and associated variability, and thereby of the optimum weighting factors, could change, although given the relatively long existing time series it may be a number of years before any such changes are appreciable. From the results of Butterworth et al. (1993) and De Oliveira (1995) it is clear that the survey information is of considerable value in managing the stock. For example, De Oliveira (1995) has estimated that, in the absence of any survey information, the average annual catch would have to be set at t to keep the probability of collapse below 25%, as opposed to t using both recruit and spawner biomass estimates. If only the spawner surveys were to be used, the average catch would have to be set at t for the same risk, and at t if only the recruit surveys were to be used. Conclusions It is concluded that the time series of estimates presented here reliably reflect the major changes in anchovy recruit and spawner biomass which have occurred in South African waters in the past 10 years. The egg-production estimates have been useful in scaling the acoustic estimates during a period when there was no reliable information on the accuracy or applicability of the target-strength expression used. However, now that methods have been developed for estimating anchovy target strength in situ, the egg-production surveys are of lesser value, especially considering that further eggproduction estimates would have little effect on the parameters in the model used to manage the resource. Despite the advantages of (indirect) A abs estimates, efforts to improve direct absolute estimates, and to quantify the errors in them better, are warranted because (a) the indirect estimates rely on the assumption of zero bias in the egg-production estimates, which is questionable, and (b) the method has limited potential for improvement without substantial increases in survey effort. In contrast, the direct estimates could be significantly improved by the reduction of, or correction for, the major biases. Finally, it is concluded that, despite their shortcomings, the surveys have produced defensible estimates of biomass and associated precision, enabling the fishery to be managed more rationally and effectively over the past 10 years than would have been possible without them. Acknowledgements I acknowledge the assistance of the officers and crew of RV Africana in the collection of the data on which this study was based, and that of all scientific colleagues who assisted in the collection and analysis of the data. References Armstrong, M. J., Shelton, P. A., Hampton, I., Jolly, G. M., and Melo, Y. C Estimates of anchovy biomass in the southern Benguela system. California Cooperative Oceanic Fisheries Investigation Report, 29: Barange, M., Hampton, I., and Soulé, M. A. Empirical determination of in situ target strengths of three looselyaggregated pelagic fish species. ICES Journal of Marine Science, 53: Bergh, M. O. and Butterworth, D. S Towards rational harvesting of the South African anchovy considering survey imprecision and recruitment variability. In The Benguela and comparable ecosystems. Ed. by A. I. L. Payne, J. A. Gulland, and K. H. Brink. South African Journal of Marine Science, 5: Butterworth, D. S. and Bergh, M. O The development of a management procedure for the South African anchovy resource. In Risk evaluation and biological reference points for fisheries management, pp Ed. by S. J. Smith, J. J. Hunt, and D. Rivard. Canadian Special Publication of Fisheries and Aquatic Sciences, 120. Butterworth, D. S., De Oliveira, J. A. A., and Cochrane, K. L Current initiatives in refining the management procedure for the South African anchovy resource. In Proceedings of the International Symposium on Management Strategies for Exploited Fish Populations, pp Ed. by G. Kruse, D. M. Eggers, R. J. Marasco, C. Pautzke, and T. J. Quinn II. Alaska Sea Grant College Program Report No , University of Alaska, Fairbanks. Crawford, R. J. M. and Dyer, B. M Responses by four seabird species to a fluctuating availability of Cape anchovy Engraulis capensis off South Africa. Ibis, 137(3). De Oliveira, J. A. A. (1995). Assessment and management of the South African anchovy resource. MSc thesis, University of Cape Town. Halldórsson, O. and Reynisson, P Target strength measurements of herring and capelin in situ at Iceland. In Symposium on Fisheries Acoustics. Selected Papers of the ICES/FAO Symposium on Fisheries Acoustics, Bergen, Norway, June 1982, pp Ed. by O. Nakken and S. C. Venema. FAO Fisheries Report, 300. Hampton, I Acoustic study on the abundance and distribution of anchovy spawners and recruits in South African waters. In The Benguela and comparable ecosystems. Ed. by A. I. L. Payne, J. A. Gulland, and K. H. Brink. South African Journal of Marine Science, 5: Hampton, I The role of acoustic surveys in the assessment of pelagic fish resources on the South African continental shelf. In Benguela trophic functioning. Ed. by A. I. L. Payne, K. H. Brink, K. H. Mann, and R. Hilborn. South African Journal of Marine Science, 12: Hampton, I., Armstrong, M. J., Jolly, G. M., and Shelton, P. A Assessment of anchovy spawner biomass off South Africa through combined acoustic and egg-production surveys. Rapports et Procès-Verbaux des Réunions du Conseil International pour l Exploration de la Mer, 189: Jolly, G. M. and Hampton, I A stratified random transect design for acoustic surveys of fish stocks. Canadian Journal of Fisheries and Aquatic Sciences, 47: Laugksch, R. C. and Adams, N. J Trends in pelagic fish populations of the Saldanha Bay region, southern Benguela upwelling system, : a predator s perspective. South African Journal of Marine Science, 13: MacLennan, D. N. and Simmonds, E. J Fisheries acoustics. Fish and Fisheries Series, 5. Chapman and Hall, London.

8 500 I. Hampton Parker, K A direct method for estimating northern anchovy, Engraulis mordax, spawning biomass. Fishery Bulletin, Washington, 78: Reynisson, P In situ target strength measurements of Icelandic summer spawning herring in the period ICES CM 1993/B: 40, 15 pp. Shelton, P. A., Armstrong, M. J., and Roel, B. A An overview of the application of the daily egg production method in the assessment and management of anchovy in the Southeast Atlantic. Bulletin of Marine Science, 53: