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ICES Journal of Marine Science: Journal du Conseil 2006 63(8):1513-1519; doi:10.1016/j.icesjms.2006.05.007
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© 2006 International Council for the Exploration of the Sea

Bycatch: complementary information for understanding fish behaviour. Namibian Cape hake (M. capensis and M. paradoxus) as a case study

Ana Gordoaa,*, Heinrich Leschb and Silvia Rodergasa

a Centro de Estudios Avanzados de Blanes (CSIC) Acc. Cala Sant. Francesc 14, 17300 Blanes (Girona), Spain
b Hangana Seafood (Pty) Ltd, Ben Amathila Avenue PO Box 26, Walvis Bay, Namibia

*Correspondence to A. Gordoa: tel: +34 972 336101; fax: +34 972 337806. e-mail: Gordoa{at}ceab.csic.es.

To identify spatio-temporal distribution in Namibian Cape hake (M. paradoxus and M. capensis), incidental hake catch by the horse mackerel fleet and targeted catch by the hake fleet were analysed for the period 1999–2004. The targeted catch, 45 955 fishing days, came from hake fishery logbooks and the incidental catch, 24 689 trawls, from observers' sample data collected aboard vessels of the horse mackerel fleet. A strong negative relationship between monthly catch rates (cpue) and bycatch was observed, confirming that the seasonal change in catchability is caused by differences in hake vertical dispersion. The October trends were an exception: both cpue and bycatch were negative. A drop in catchability at different depths of the fishing grounds indicates that M. capensis migrates to shallower water (<200 m) at the peak of spawning. Although there was no significant relationship between annual catch rates and bycatch, probably because of the short length of the time-series, annual bycatch should not be discarded as an indicator of hake recruitment. The results highlight the potential informative component of bycatch in identifying population patterns that cannot be extracted from the targeted catch.

Keywords: bycatch, catchability, distribution, hake, northern Benguela

Received 3 March 2006; accepted 24 May 2006.


   Introduction
Top
 Introduction
Material and methods
 Results and discussion
 Conclusions
 References
 
Bycatch and discards have received a great deal of scientific attention, their minimization being a goal of marine fisheries management (Powers, 2006). Consequently, most studies deal with optimization of fishing efficiency and minimization of fishing impact, but bycatch data have rarely been used to learn about the biology and behaviour of the incidental species being caught, although several recent studies have shown the informative value of bycatch concerning food habits (Koen Alonso et al., 2001), feeding ecology (Rheeder and Sauer, 1998), and recruitment indices (Payne et al., 2005).

The life history of many fish species involves changes in their distribution through the water column. Additionally, many exhibit periodic vertical migrations associated with feeding or spawning. To comprehend the behaviour of fish populations exhibiting variable patterns in their vertical distribution, collecting data from the whole vertical distribution range is fundamental. Populations with a broad vertical distribution are available to different fleets, implying that they can be subjected to both targeted fishing and bycatch. Monitoring of such fish populations should consider gathering data on both targeted and incidental catch. Hake species can be included in this category of populations: species of the genus Merluccius are characterized as nektonic (Cohen et al., 1990), with a broad spatial distribution (Pitcher and Alheit, 1995). Different types of migration have been observed in hake – from diurnal vertical migrations for feeding (Inada, 1981; Payne et al., 1987) to seasonal horizontal ones for spawning (Bailey et al., 1982; Dorn, 1995; Macchi et al., 2005).

Namibian waters are influenced by the Benguela upwelling system of the Southeast Atlantic, and their most commercially valuable demersal fish resource is hake. The Namibian hake fishery is for two species, which are similar in appearance (shallow-water M. capensis and deep-water M. paradoxus), and they are not distinguished in commercial hake catches. Since 1990, when the country achieved independence, the demersal trawl fishery has accounted for some 90% of the total hake catch, but the resource is also included in the bycatch of the monkfish, sole, and horse mackerel fleets (Van der Westhuizen, 2001).

The hake species of Namibia and South Africa have been the subject of numerous studies (Gordoa et al., 1995; Payne and Punt, 1995; Burmeister, 2001; Voges et al., 2002). Earlier studies in Namibia from monitoring programmes revealed high seasonal variability in research catch rates (Macpherson et al., 1991), a feature also observed for other species (Macpherson and Gordoa, 1992) caught during the surveys, species taken as bycatch in the hake fishery. Later studies based on commercial logbook data corroborate the belief that the variability in hake catchability had a clear seasonal pattern that matched the sea surface temperature (SST) cycle (Gordoa et al., 2000b). The covariability between environmental and hake catchability cycles was explained by the dynamics of hake vertical distribution. Thus, seasonal catchability patterns were described as a spatial response to seasonal environmental variability through a vertical- or bottom-compressed scattering, like an accordion movement. Horizontal migration was not observed, because catchability varied throughout the region without temporal shifts. On the contrary, in South Africa, both hake species migrate horizontally: inshore and southwards (Payne, 1995; Millar and Field, 2002). Nevertheless, off Namibia, catchability was always lowest in October, coinciding with the spawning peak of shallow-water hake (M. capensis; Botha, 1986). As both hake species are mesopelagic spawners (Sundby et al., 2001), active spawners are not vulnerable to bottom trawl activity (Botha, 1986), and hake vertical dispersion may be driven by both flow and spawning dynamics.

In this study, we test the hypothesis of the monthly "accordion motion" of the Namibian hake resource through its incidental catch by the horse mackerel fleet. We presume that differences in hake vertical scattering may result in variation in that incidental catch, but if this assumption is true, hake catchability by the horse mackerel fleet (midwater trawls) will vary inversely with hake catchability by the hake fleet. The hypothesis was tested by analysing the monthly catch rates of hake in the horse mackerel and hake fleets using daily catch data over five consecutive years (1999–2004). We also seek to examine the information value of bycatch data.


   Material and methods
Top
 Introduction
 Material and methods
Results and discussion
 Conclusions
 References
 
Data on the hake bycatch in the horse mackerel fleet came from a scientific observer programme aimed at monitoring the fishing operations of Namibian fishing fleets, including the collection of biological data and catch information (including bycatch). Here, we analyse the incidental catch of hake in every trawl sampled by observers aboard horse mackerel vessels between January 1999 and March 2004. The total number of sampled trawls was 24 689 and the information on each trawl was licence number, vessel gross registered tonnage (grt), date, depth (m), latitude, longitude, horse mackerel catch (kg), and hake bycatch (kg), if any. The study area is shown in Figure 1.


Figure 1
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  		Figure 1 The study area off Namibia.

 
Prior to analysis, the bycatch of hake was standardized, owing to the large differences in tonnage within the horse mackerel fleet (715–7765 grt), by dividing hake incidental catch by vessel fishing power. Fishing power was estimated on the basis of differences in horse mackerel (target species) catch rates. First, the average catch per trawl was estimated over the whole time-series for each vessel. The fishing power of the vessel selected as standard was considered equal to one and the fishing power of the other vessels was estimated by dividing their average catch by the average catch of the standard vessel. The unit of standardization had to be trawl rather than a true unit of fishing effort (hour or day) because the observer data lack information on the duration of each trawl. Differences in fishing power per trawl should be higher than those estimated per trawling hour, because to differences in gear dimension must be added differences in trawling time. Nevertheless, for the purpose of the present study, the trawl unit suffices.

Information on hake-directed catches came from the bottom trawl hake fishery logbooks collected by Namibia's National Marine Information and Research Centre (NatMIRC) between January 1999 and March 2004. The total number of fishing days was 79 631. The daily information included in each record was vessel licence number, date, mean daily latitude and longitude, average depth (m), the number of trawls made, the number of hours trawled per day, and the daily catch of hake (kg). The standardized catch per unit effort (cpue) was calculated for each individual record, and effort (fishing hour) was standardized by tonnage class of vessel. Although the fishing grounds of the hake fleet extend from 17°S to 29°S, for comparative purposes we used only the information from hake trawlers operating within the same area as the horse mackerel fleet (17–25°S). Therefore, the number of fishing days analysed was reduced to 45 955.

The mean monthly cpue of hake was estimated for both hake and horse mackerel fleets. The monthly pattern of hake incidental catch by the horse mackerel fleet can be taken as the monthly mean bycatch, but a clearer or complementary pattern can be obtained from the monthly catch probability. The monthly probability (Pmt) of obtaining an incidental catch of hake in a horse mackerel trawl was estimated from


Formula

where Nhm is the number of trawls sampled that had hake in the catch in month m and Ntm is the total number of trawls sampled in month m. Regression analysis was applied to explain the monthly variability in cpue of the hake fleet through hake bycatch and its probability.

In order to provide an approximation of horse mackerel fleet impact on hake, the monthly incidental catch per trawl (ICmt) was estimated from


Formula

where BCmt is the monthly average bycatch per trawl.


   Results and discussion
Top
 Introduction
 Material and methods
 Results and discussion
Conclusions
 References
 
Time-series of monthly hake cpue by the hake fleet were significantly correlated with both hake bycatch and bycatch probability of the horse mackerel fleet (Spearman correlation coefficients were –0.34 for bycatch, and –0.35 for bycatch probability). Although the correlations were significant at p < 0.05, the low values may result from merging the annual and monthly variability and their corresponding factors. Therefore, monthly variability could be distorted by the interannual variability in hake abundance.

Averaging all time-series by month, there is an opposing pattern between hake fleet catch rates and hake bycatch and bycatch probability (Figure 2). Although the opposing pattern was clearer with bycatch probability, the probability of hake catches by the horse mackerel fleet increased from January to September while catch rates of the hake fleet decreased. This confirms our initial hypothesis: the decrease in hake density at the seabed is due to hake increasingly rising off the bottom then. However, there was one exception: from September to October, catch and bycatch decreased simultaneously. The opposing trend was repeated after October. The increase in hake bycatch by the horse mackerel fleet until September explained the decrease in hake fleet catchability. The linear regression model explained 80% of bycatch probability through monthly catch rates by the hake fleet (Figure 3). These results are the first indication of a monthly increment in vertical dispersion of Namibian hake from austral summer to spring, so corroborating earlier opinions. Earlier studies showed that the monthly hake catchability is related to environmental variability, catchability peaking when there is little upwelling, and dropping close to its minimum when upwelling is intense (Gordoa et al., 2000b). These results suggest that the Namibian hake population responds to seasonal changes in physical parameters. M. productus in British Columbia is also affected by shelf-break location, but dispersion is greater when upwelling is less (Mackas et al., 1997).


Figure 2
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  		Figure 2 Monthly average catch rates of hake: (a) monthly average hake bycatch per trawl by the horse mackerel fleet and (b) monthly probabilities of hake presence in a horse mackerel trawl.

 


Figure 3
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  		Figure 3 Monthly catch rates of hake plotted against the probability of hake presence in a horse mackerel trawl.

 
Although we can explain monthly changes in hake catchability by changes in the vertical distribution, the exception, i.e. from September to October, is of interest because it reflects an annual minimum in hake catchability, independent of interannual environmental variability at the same time (Gordoa et al., 2000a). During October, hake abundance decreases in the midwater layers targeted by the horse mackerel fleet and on the seabed targeted by hake trawlers. This raises the question "where does the hake population go in October?" Horizontal migrations across fishing grounds were rejected in previous studies because the same seasonal trend of a low catch rate takes place at all depths (Gordoa et al., 2000a). On the other hand, vertical migrations within the fishing grounds used by the horse mackerel fleet are rejected here. However, the region analysed here is limited by the 200-m depth contour, within which no trawling is currently permitted. As a result, our comments relating to a lack of horizontal or vertical migration during October can only be made for water deeper than 200 m. The drop in the October hake catch, at least for shallow-water hake, could be related to spawning behaviour, which peaks around then (Botha, 1986). Further, the distribution of eggs and larvae indicate that spawning takes place mainly inside the 200-m isobath (O'Toole, 1978; Olivar et al., 1988; Olivar and Fortuño, 1991) and in midwater (Botha, 1973). Recently, Sundby et al. (2001) found a wider depth range in the distribution of eggs (100–400 m), but Stage I hake eggs were still mainly located around Namibia's 200-m isobath. We therefore suggest that although shallow-water hake may spawn over a wide range of depths, peak spawning would take place in water shallower than 200 m, which would then explain the drop in catch rates of both fisheries at that time.

As deep-water hake are distributed deeper in the latitudinal range studied here, it is unlikely that they significantly contribute to the hake bycatch of the horse mackerel fleet. Therefore, if they do contribute to the observed drop in catch rate of both the fisheries in October, their effect should be less significant than that of shallow-water hake. Moreover, it is unlikely that deep-water hake would migrate inshore, because there is no evidence of M. paradoxus spawning off Namibia (Gordoa et al., 1995; Sundby et al., 2001), although their southward migration through midwater layers could be feasible.

An inshore spawning migration within the 200-m isobath is common in other species of hake. M. merluccius off Northwest Africa (Maurin, 1954; Ehrlich, 2000) spawn in waters shallower than 200 m, and Patagonian hake (M. hubbsi) historically spawn in coastal waters at depths of about 50–100 m (Macchi et al., 2005). M. merluccius in the Adriatic changes its spawning depth as the spawning season progresses, from depths >150 m to depths of <150 m, the shallowest waters being occupied at the spawning peak (Zupanovic and Jardas, 1986). The results of this study indicate that M. capensis may exhibit behaviour similar to the Adriatic population, peak spawning occurring in shallower water.

Incidental hake catches by the horse mackerel fleet per month and per trawl vary between 269 kg and 1314 kg, the mean being around 750 kg, with a CV of 53% (Table 1). The results show clearly that the impact of the horse mackerel fleet on hake will vary, depending on the temporal distribution of its effort. Therefore, the annual incidental catch of hake estimated as the average of monthly means will only be unbiased if the fishing effort of the horse mackerel fleet was evenly distributed throughout the year, an unlikely situation. We therefore consider that future studies on total hake incidental catch by the horse mackerel fleet should be estimated on a monthly basis.


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  		Table 1 Monthly hake bycatch by the horse mackerel midwater trawl fleet.

 
The annual estimates of bycatch provide a very different picture of the relationship between hake catch and hake bycatch. Bycatch variables, the probability of being caught and the catch, correlate significantly (r = 0.89, p < 0.05), meaning that if the annual quantity of hake taken in midwaters increases, the horizontal area occupied by hake off the seabed increases proportionally. The data from 2004 were not considered in the previous correlation because the time-series ends in March 2004, so, cannot be representative of the annual bycatch variables. Unexpectedly, bycatch variables did not correlate significantly with catch rates of the hake fleet, so, a high hake bycatch cannot be expected just because direct hake catch rates are high, or the opposite. The apparently independent annual trends may reveal that hake off the seabed caught by the horse mackerel fleet and hake close to the bottom caught by the hake trawl fleet represent different segments of the hake population. However, looking more closely at the annual trends, we see that the year after a high bycatch by the horse mackerel fleet (2002, 2003), the catch rates of the hake trawl fleet increase (2003, 2004; Figure 4). Therefore, we suggest that the annual level of hake bycatch may depend on the strength of the incoming young of the year, which are recruited one year later to the demersal hake fishery. The shortness of the available time-series and the lack of hake bycatch size frequency information prevent us from developing this thesis further, but it should be taken into account when planning future monitoring programmes.


Figure 4
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  		Figure 4 Annual time-series of mean catch rate and mean bycatch of hake.

 

   Conclusions
Top
 Introduction
 Material and methods
 Results and discussion
 Conclusions
References
 
In this study, the information value of bycatch data was confirmed, providing the complementary information necessary to understand hake spatio-temporal behaviour and its effect on hake catchability. We confirm that monthly changes in the catchability of Namibian hake vary almost inversely with its incidental catch in the horse mackerel fleet, specifically from summer to spring, following the upwelling cycle. Additionally, we have learned that hake catch and bycatch decrease in October, so hake density decreases at the seabed and in midwater, coinciding with the spawning peak of shallow-water hake. We conclude that shallow-water hake, at least during the spawning peak, move to shallower water to spawn (<200 m). The annual time-series analysed was short, so, results on annual trends are not very illustrative, but the possibility of using annual bycatch strength as an index of hake recruitment cannot be rejected. We venture to provide an estimation of bycatch harvesting rate per trawl, because as with the other results, our confidence is based on thousands of trawls having been analysed.

Traditionally, commercial data, despite its volume, have been considered unreliable because differences in spatial and temporal coupling between fish and fisheries may give a misleading picture of stock trends. We believe that this generalization stems from incorrect use of commercial data in which the spatial and temporal dynamics of the fishery (fish and fleet) have been disregarded. This trend is changing, and appropriate use of commercial statistics is revealing their information potential. The spatial and temporal coverage of such data may be the only source of reliable information for monitoring species that exhibit variable spatial distribution. We conclude that for this type of species, monitoring of the populations should include studying several fleets, and not be limited to the target fishery. In agreement with Hilborn and Walters' (1992) statement that stock assessment involves understanding the dynamics of fisheries, our results highlight the plurality of the latter.


   Acknowledgements
 
We thank Paul Martin for correcting the English of the manuscript, and the Agencia Española de Cooperación Internacional for financial support. Additionally, we are grateful to Hector Cordo and Rob Leslie for their constructive reviews and valuable comments on the submitted draft.


   References
Top
 Introduction
 Material and methods
 Results and discussion
 Conclusions
 References
 

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