Butterworth, D.S. (1988), A simulation study of krill fishing by an individual Japanese trawler

A model is set up for the operation (which includes both searching and fishing) of a Japanese krill trawler over a half-month period. It is based on an underlying krill distribution model whose parameters are determined primarily from the scientific FIBEX surveys. Output from the model of the operation is compared with (and partially tuned to) statistics for a sample of data from the commercial fishery. A major inconsistency is found: haul times are a factor of 4-5 times greater in reality than in the model. Two ad hoc model modifications are introduced to eliminate this inconsistency: artificially elongating krill swarms, and allowing hauls to continue through more than one swarm. Twenty four candidate abundance indices (generally of a CPUE form) for krill biomass in the 600 n mile square oceanic sector modelled are considered, and their performance in response to a variety of ways in which the overall krill biomass might decline is investigated. Generally the indices respond by dropping relatively less than the proportional biomass decrease. Catch statistics collected at present (centred primarily on catch per fishing time) are of low utility in detecting biomass decline. Combination catch rate indices incorporating within-concentration search time give improved performances, but are able to monitor changes in within-concentration krill distribution parameters only. Indices that distinguish primary searching time from secondary searching time (searching while waiting to finish processing) within concentrations perform better, but collection of the requisite data may not be practical. Other approaches (e.g. research vessel surveys) need to be considered to monitor changes in the number, distribution and size of krill concentrations, both because there are doubts about the reliability of indices based on concentration searching time (which do respond to such changes), and because such indices are relatively imprecise. Priority needs to be given to improving the krill distribution model underlying the analysis; this probably requires that scientific surveys be planned to operate in small areas concurrently with fishing vessels.

Butterworth, D.S. (1988), Some aspects of the relation between Antarctic krill abundance and CPUE measures in the Japanese krill fishery

The history of the Japanese krill fishery is reviewed briefly. Important aspects of the fishing operation are the constraints imposed by processing rate limitations on the vessels, and product quality considerations - in particular the increasing tendency to avoid catching “green” krill. These factors result in Catch-per-Day and Catch-per-Haul measures being unlikely to index krill abundance. During the high season, Catch-per-Towing-Time seems likely to index only within-swarm density. Search time data may be needed to assess the density of swarms in a concentration, but may be difficult to record in practice, and a number of other factors may complicate any analysis. The possibility of indexing the extent of the krill distribution through routine oceanographic monitoring merits attention. A data sample from the Japanese krill fishery statistics data-base has been selected for further studies.

Mangel, M. (1988), Analysis and modelling of the Soviet Southern Ocean krill fleet

The first part of this document contains an analysis of data pertaining to the Soviet krill fleet. The data base consists of the records of 12 different cruises by 8 different research vessels between 1981 and 1984. The data are analyzed according to operational characteristics of the fishing process such as trawl duration, krill catch, or between trawl movement. Correlation analyses are presented as a means of understanding the within trawl and between trawl features of the operation. The data support the notion of a “patches within patches” model for the distribution of krill in the southern oceans.

The second part of this document contains the development and use of a simulation model of a Southern Ocean krill fleet. The objective of the work is to answer questions such as: what information do catch and effort data provide about krill abundance or how easily can significant changes in krill biomass be detected? The krill distributional model begins with individual krill which are assumed to aggregate into swarms of krill. The swarms then aggregate into concentrations, which are the foci for the fishing operation. Parameters of the model are motivated by study of the literature and FIBEX results. A model is developed for a survey vessel that does no fishing, but simply locates concentrations of krill for the fishing fleet. The fishery model involves finding concentrations, finding swarms within concentrations and fishing individual swarms. Wherever possible, operational data from Part I are used to provide distributions in Part II. General considerations about the theory of abundance indices for pelagic, schooling species are discussed. In particular, the importance of the time spent searching for swarms is highlighted. A theory for detecting changes in krill biomass is developed. Forty-four different abundance indices are considered and their effectiveness in detecting changes in krill biomass is studied. The best indices involve two separate measures: one in which survey vessel discoveries are used to track the number of concentrations and a measure of the form catch/swarm/search-time to track swarm density within concentrations and krill density within swarms. Operational recommendations are given: (i) I propose an experiment in which survey and fishing vessels operate simultaneously but independently in the same region, (ii) I recommend that fishing vessels begin to indicate in their log books the amount of between trawl time spent searching, (iii) I propose that CCAMLR consider sending a Ph.D. level modeller to sea in order to develop a truly operational model of the fishing process, and (iv) I propose abundance indices that could be used to track krill biomass.

Dolzhenkov, V.N., T.G. Lubimova, R.R. Makarov, S.S. Parfenovich and V.A. Spiridonov (1988), Some specific features of the USSR krill fishery and possibilities of applying fishery statistics to studies of krill biology and stocks

General principles of the USSR krill fishery such as the location of exploited fishing areas and the seasonal regime of their exploitation are considered. Using data obtained by the scouting vessel Globus engaged in regular krill fishery, it is shown that the catch-per-haul variables are associated with the fishing regime of the vessel rather than with krill abundance in a certain area. During preparations for regular fishing operations very short hauls (under 15 minutes) are practised. Such fishing practice, together with substantial fluctuations of catches during scouting operations often results in yields which do not correspond to the actual biomass of krill in the place in question. In both cases particular diurnal and long-term behaviour patterns have an impact. All these factors limit the extent to which CPUE can be used in simulation studies of krill distribution and stock assessment. A standard large-scale multi-disciplinary survey, followed by processing of the data obtained using diverse methods may be viewed as a better instrument for studies.

Everson, I., I. Hampton and G.M. Jolly (1988), Survey design to estimate krill abundance during FIBEX

One of the primary aims of FIBEX (First International BIOMASS Experiment), 1981 was to study the methodology for assessing the abundance of krill. The survey design used in the southwest Atlantic study area of the FIBEX is described in this paper. Sampling involved the use of echo sounders for estimating krill abundance as well as collection of data on the size, density and distribution of krill swarms. In addition, information on surface water temperature, salinity and fluorescence as well as on seabirds was also collected. The study area was subdivided into several geographically distinct subareas in each of which randomly spaced transects were located. Subareas were treated as strata and a stratified random sampling method was used. The survey was done in two phases. In the first phase a fairly evenly dispersed subsample of transects was surveyed and these were also used to fix stratum boundaries. In the second phase the remaining transects were surveyed, using the stratum boundaries defined from the first phase. The design of the survey was directly related to the subsequent method of data analyses, some main aspects of which are discussed. The analytical formulae for the analyses are also presented

Everson, I., D.G. Bone, J.L. Watkins and K.G. Foote (1988), Target strengths of Antarctic krill (Euphausia superba)

The Mean Volume Backscattering Strength of encaged aggregations of swimming krill have been measured at 38 and 120 kHz in a sheltered bay at South Georgia. The results indicate that the Target Strength values are approximately 10dB lower than previously assumed.

Levin, S.A., A. Morin and T.M. Powell (1988), Patterns and processes in the distribution and dynamics of Antarctic krill

A general framework is presented to develop, test and integrate component models of the distribution and dynamics of Antarctic krill population at various spatial and temporal scales. We suggest that models of increasing complexity be developed iteratively for variability and patchiness of krill abundance. Incremental models should then be compared to statistical descriptions of the observed distribution patterns at various scales of observation to ascertain the plausibility of the model and identify critical processes to be added. An analysis of spatial distribution of krill in the Bransfield Strait area reveals that purely physical models of turbulent redistribution are not sufficient to explain krill distribution at small scales. We therefore propose to develop a modified diffusion-reaction model incorporating spatially variable growth rates of krill, krill loss rates due to predators, and density-dependent attraction of krill to account for the small-scale aggregations.

Macaulay, M.C. (1988), Statistical problems in krill stock hydroacoustic assessments

Two primary issues are at question for hydroacoustic assessments of krill. The first is the methods applied to establish biomass in a survey area and the second is the improvement in accuracy of target-strength measurements. In the case of statistical methods, there are no clear guidelines for deciding what method is most appropriate, this is made even more difficult by the fact that most survey methods assume the population is fixed in space, relative to the sampling interval. There remain several unsatisfied needs for improvements in sampling design and tests for systematic trends in survey data collected from non-stationary populations, which have not been well addressed by present techniques. However, this does not invalidate the use of available methods to conduct surveys and analyze results. In the case of target-strength accuracy, even if the present values were very accurate, the issue of interest would seem to be not the absolute amount of biomass present in an area, but rather how it is distributed. The issue of patchy years vs more even distribution would seem to have more impact on ecosystem management than absolute accuracy of biomass estimates.

Morin, A., A. Okubo and K. Kawasaki (1988), Acoustic data analysis and models of krill spatial distribution

Acoustic data obtained on 4-5 January 1987 aboard the R/V Professor Siedlecki were used in three descriptors of krill spatial aggregation: power spectra for krill biomass fluctuations in space, semivariogram (spatial autocorrelation of krill biomass) and the frequency distribution of krill biomass estimate. The wavenumber spectrum resembles a white noise at scales of 2-20 km, although at scales smaller than 1 km the spectrum appears to lose its power significantly. The semivariance of biomass does not vary significantly over most distances between points except for the distances smaller than 1 km. The computed frequency distribution of krill biomass is bimodal and appears to be the mixture of two log normal distributions. These two distributions may correspond to the between and within patch biomass. These results of data analysis suggest that krill patch size or rather a basic swarm size should be smaller than 200 m, finest resolution of our data analyzed, and the real spatial distribution of krill should be the manifestation of the balance between the dispersion of the basic swarm units and long-range density-dependent attraction of the units. Simple dynamical and kinematical models can interpret the observed result.

Yamazaki, H. and T.R. Osborn (1988), Phytoplankton distribution in the mixed layer: implication to krill abundance

A one dimensional Lagrangian model of random walk is presented to study the distribution of phytoplankton in the Antarctic Ocean. Since little is known about mixed layer dynamics in the Antarctic Ocean, we estimate the depth of the mixed layer and its turbulence intensity from an Ekman layer model. Available CTD data suggest that the mixing in the upper layer is less than what we expected. However, the effect on the dynamics is vital, affecting the distribution of particles in the upper ocean.

Macaulay, M.C. (1988), Joint Polish/American hydroacoustic survey of Elephant Island and the vicinity of King George Island, 1988

The hydroacoustic survey found a low krill abundance in most areas covered by last year’s survey. The total biomass in the vicinity of Elephant Island was estimated from 120 kHz data to be 260k tonnes and that in the Bransfield Strait south of King George Island was 39k tonnes for a total of 299k tonnes in the combined areas. The estimated 200 kHz survey data were higher, giving 715k tonnes near Elephant Island and 83k tonnes in the Bransfield Strait. The survey results apply to 7 453 n miles² near Elephant Island and 2 894 n miles² in the Bransfield Strait. The full survey found (120 kHz data) 385k tonnes (in 7 787 n miles²) in the Bransfield Strait and the area north of King George Island and 309k tonnes (in 8 836 n m²) in the expanded area around Elephant Island.