Micromesistius australis EN NUEVA ZELANDA
by
S. M. HANCHET1, V. HAIST2and D. FOURNIER3
1NIWA, PO Box 893, Nelson, New Zealand (e-mail s.hanchet@niwa.cri.nz)
2Pacific Biological Station, Department of Fisheries and Oceans, Nanaimo, British Columbia, Canada, V9R 5K6
3Otter Consulting Ltd., PO Box 265, Nanaimo, British Columbia, Canada, V9R 5K9
Summary
The southern blue whiting (Micromesistius australis) fishery is one of the largest fisheries in New Zealand waters, with landings peaking at over 75 000 t in 1992. A separable Sequential Population Analysis was developed to simultaneously analyse the acoustic, catch-at-age, and CPUE data. Simulated data were used to estimate confidence limits, and included uncertainty in the annual catch, catch-at-age, CPUE, and acoustic data. The model results suggest that the stock underwent a major decline during the 1980s and early 1990s but has since recovered , due mainly to the recruitment of the strong 1991 year class. However, the extent of the recovery is uncertain largely because of observation error in the tuning indices and the sensitivity of the model to natural mortality
Introduction
The southern blue whiting (Micromesistius australis Norman) fishery is one of the largest fisheries in New Zealand waters. It was developed in the early 1970s by the Russian fleet and since then landings have fluctuated considerably averaging about 20 000 t, and peaking at over 75 000 t in 1992. Research on this fishery started in the late 1980s and the first stock assessment was carried out in 1991 (Hanchet 1991). Catch-at-age data and CPUE indices were developed during the early 1990s and acoustic surveys of adult and pre-recruit abundance were started in 1993 (Ingerson & Hanchet 1996). A separable Sequential Population Analysis was developed in 1994 to integrate all the available data in one stock assessment (Hanchet & Haist 1994). This paper summarises the model and model results, and is a shortened version of a
paper presented in Hanchet et al. (1998).
Methods
The model developed to analyse the fishery is an extension of Fournier and Archibald (1982). It uses a maximum likelihood method to find the set of parameter values which minimises the sums of squares between observed and expected catch, proportion-at-age, fishing effort and adult acoustic biomass indices. The likelihood from each input data set is weighted using values that represented our levels of confidence in the data source. An estimate of the confidence came from a consideration of both the estimated variance and possible bias inherent in the data. Where appropriate, weights were assigned different values between years. Full documentation of the model is provided in Hanchet et al.(1998).
Table 1. Biomass (t) and c.v. (%) of adult and pre-recruit (mainly 2 year old) southern blue whiting from acoustic surveys of the spawning and nurs-ery grounds on the Campbell Island Rise (Ingerson & Hanchet 1996).
Adult Pre-recruit Biomass cv Biomass cv 1993 18 500 21 89 600 23 1994 161 400 36 22 400 38 1995 121 100 30 20 000 25
Natural mortality (M) was assumed to be 0.2 and to be constant for all ages and years. Weight at age was calculated from the weight-length relationship and von Bertalanffy growth coefficients given in Hanchet (1991) and assumed to be constant for each year. Simulated data were used to estimate confidence limits, and included uncertainty in the annual catch, catch-at-age, CPUE, and acoustic data.
A number of sensitivity analyses were carried out to examine the sensitivity of the model results to alternative model assumptions. These included the relative weightings given to the catch-at-age and effort data, M, and whether the acoustic indices were treated as relative or absolute.
The model was implemented using AD Model Builder software (Fournier 1994), which gave simple and ready access to minimisation routines, and provided the ability to estimate the variance-covariance matrix for all dependent and independent parameters of interest. The parameters being estimated when minimising the negative log-likelihood function were numbers in the initial population and subsequent recruitment, in addition to various selectivity parameters and abundance scalars (see Hanchet et al. 1998).
Results and discussion
The biomass trajectory and 90% confidence intervals are plotted in Fig. 1. The results s uggest
that the stock underwent a large decline during the 1980s and early 1990s but has since recovered, due mainly to the recruitment of the strong 1991 year class. The wide confidence limits suggest that estimates of current biomass and the 1991 year class are highly uncertain.
However, independent supporting evidence of its size comes from the large pre-recruit biomass in the 1993 acoustic survey (Table 1), and the very slow growth rate of this year class (Hanchet 1997). The model suggests that this year class is at least 3 times the size of the strong 1979 and 1980 year classes, and about 10 times the average.
Fig. 1. Mid-season spawning stock biomass (t) with 90% confidence intervals, showing the fit to the three adult acoustic survey indices.
The fit of the model to the adult acoustic biomass is shown in Figure 1. The value of the abundance scalar (q) was 1.14, indicating reasonable agreement between the absolute adult acoustic estimate and population biomass.
The results of the sensitivity analysis are shown in Table 2. The results were relatively insensitive to the weightings examined for the catch-at-age and effort data. Estimates of historic biomass and the size of the 1991 year class were most sensitive to the value of M.
Future improvements to the model will include the use of an annual weight at age matrix, the incorporation of ageing error, and the fitting of age structured acoustic indices.
Seminario Final Proyecto INIDEP - JICA, 1999 195 Table 2. Relative changes (expressed as
percentages) of selected parameter estimates as a result of alternative model assumptions for the stock. B, mid-season spawning stock biomass;
R1991, size of the 1991 year class.
Model B1980 B1988 B1995 R1991 Sample size = 325 0.0 0.1 2.0 1.6 Sample size = 50 -0.1 -0.1 -1.8 -1.3 CPUE cv = 70% 0.1 0.5 9.1 7.3 M = 0.15 -24.7 -9.2 -9.1 -20.0 M = 0.25 37.3 11.3 11.2 26.4 Acoustic index absolute 0.3 0.9 11.8 9.5
Acknowledgements
This project was funded by the New Zealand Ministry of Fisheries project number SBW9701.
References
Fournier, D. 1994. An introduction to AD Model Builder - for use in nonlinear modelling and statistics. Otter Research Ltd. Canada. 51 pp.
Fournier D., & C. P. Archibald. 1982. A general theory of analysing catch-at-age data. Can. J.
Fish. Aquat. Sci. 39: 1195-1207.
Hanchet, S. M. 1991. Southern blue whiting fishery assessment for the 1991-92 fishing year. N. Z. Fisheries Assessment Research Document 91/7. 48 pp.
Hanchet, S.M. 1997. Southern blue whiting (Micromesistius australis) fishery assessment for the 1996-97 and 1997-98 fishing years. N.
Z. Fisheries Assessment Research Document 97/14. 32 pp.
Hanchet, S. M., Haist, V. & Fournier, D. 1998: An integrated assessment of southern blue whiting (M. australis) from New Zealand waters using separable Sequential Population Analysis. In:
Funk, F., T. J. Quinn II, J. Heifetz, J. N. Ianelli, J. E. Powers, J. F. Schweigert, P. J. Sullivan, &
C. -I. Zhang, (eds) Fishery stock assessment models. Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, 1998.
Ingerson, J. K. V. & S. M. Hanchet. 1996.
Acoustic biomass estimates of southern blue whiting (Micromesistius australis) from the Bounty Platform, Pukaki Rise, Campbell Island Rise, and Auckland Island Shelf, August-September 1995. N. Z. Fisheries Assessment Research Document 96/18. 29 pp.