ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on June 26, 2008
ICES Journal of Marine Science: Journal du Conseil 2008 65(7):1122-1130; doi:10.1093/icesjms/fsn105
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Integrating commercial and research surveys to estimate the harvestable biomass, and establish a quota, for an "unexploited" abalone population
South Australian Research and Development Institute (Aquatic Sciences), PO Box 120, Henley Beach, South Australia 5022, Australia
Correspondence to S. Mayfield: tel: +61 8 8207 5427; fax: +61 8 8207 5406; e-mail: mayfield.stephen{at}saugov.sa.gov.au.
Mayfield, S., McGarvey, R., Carlson, I. J., and Dixon, C. 2008. Integrating commercial and research surveys to estimate the harvestable biomass, and establish a quota, for an "unexploited" abalone population. – ICES Journal of Marine Science, 65: 1122–1130.A key challenge facing many fisheries managers is the absence of information on the level of harvestable biomass. We describe an integrated, two-stage survey approach that was used to measure the spatial distribution and harvestable biomass of a largely unexploited metapopulation of greenlip abalone (Haliotis laevigata) over a large area of northwestern Spencer Gulf, South Australia. In stage 1, commercial fishers conducted systematic surveys to identify subareas with abalone at harvestable densities. Cpue measures from these surveys were used to map and stratify a bounded survey subregion, within which leaded-line, research-diver surveys measured absolute density and harvestable biomass (stage 2). Decision tables, showing minimum biomass at various probabilities vs. harvest fraction, were developed to provide a risk-assessment framework for quota setting. Within two years, our approach allowed, first, the mapping of the broad-scale, spatial distribution and abundance of greenlip abalone in an area of 1143 km2, second, the estimation of harvestable biomass in a smaller (16.9 km2) area, and finally, the allocation by State fishery managers of an additional quota inside a newly defined management subzone. The collaborative approach we describe for providing estimates of absolute biomass over large spatial scales affords multiple advantages for the assessment and management of invertebrate dive fisheries.
Keywords: abalone, commercial survey, harvestable biomass, quota setting
Received 23 November 2007; accepted 5 May 2008; advance access publication 26 June 2008.
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Introduction |
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 Top Introduction Material and methodsResultsDiscussionReferences |
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A measure or a model estimate of total and harvestable biomass is among the most useful quantities that stock assessments can provide to fishery managers, especially in quota-managed fisheries. In developing fisheries, information on the level of harvestable biomass is seldom available, primarily because of the absence of data. This key challenge facing those responsible for developing fisheries means that policy regarding their management is typically precautionary (Garcia, 1994; FAO, 1996).
Perry et al. (1999) proposed a comprehensive, three-phase framework for the management of new and developing invertebrate fisheries that explicitly endorses the precautionary approach. This entails, by definition, controlling exploitation (e.g. Navarte, 2006), reducing the risks of overfishing and overcapitalization, and preventing fisheries from developing faster than the rate at which they can be assessed and managed (Hilborn and Sibert, 1988; Hilborn, 1997; Walters, 1998). Such approaches are, however, not always successful (Boyer et al., 2001), and they also limit the potential for economic opportunities to be optimized (Smith, 1993).
The progress of developing fisheries often includes increased reliance on stock-assessment models for determining stock status, thus informing management strategies such as sustainable yields and developing reference points (Walters and Pearse, 1996; Caddy, 1998, 2004). Typically, this involves fitting mathematical models to data and information collected by the fishery, to infer absolute population size, and to reconstruct the history of the exploited populations (Hobday and Punt, 2001; Breen et al., 2003; Gorfine et al., 2005). Although some simple approaches may be informative (Gaertner et al., 2001), in practice, formal assessment modelling requires time and comparative data (Patterson et al., 2001), to the extent that the resource may have already been overexploited before formal assessment advice becoming available (Walters, 1998; Perry et al., 1999). Moreover, virgin biomass is typically poorly estimated by fisheries models (Punt, 2003). Although the Bayesian decision analyses may partly address this issue (McAllister and Kirkwood, 1998), the problem is exacerbated by low levels of both data and data diversity (Chen et al., 2003), model sensitivity to parameter mis-specification or the use of inappropriate spatial scales (Punt, 2003; Naylor et al., 2006), targeting behaviour by fishers (Gorfine and Dixon, 2001; Ellis and Wang, 2007) and heavy reliance on fishery-dependent data. Many of these factors are common to developing fisheries, and reduce levels of certainty in such assessments.
The challenge with relying on population-dynamics models to inform fishery-management decisions (Bechtol and Gustafson, 1998), especially during the early years of a fishery, can be overcome through more direct measures of biomass. Where they are available, estimates of harvestable biomass enable a quota, with quantified risk, to be set before commercial fishing (McGarvey et al., in press). This approach allows rapid implementation of a regulated fishery, with controlled risks of overexploitation or overinvestment, or a mixture of both these factors, and removes some of the pressure on fishery managers asked to approve such new fisheries (Perry et al., 1999).
Biomass estimates from acoustic (Marques et al., 2005), egg-production (Ward et al., 2001), and trawl (Fritz and Brown, 2005) surveys are common for finfish. Although less widespread, methods of diver survey have been developed to measure benthic invertebrate biomass inside bounded survey regions directly, using transect counts and length measurements (Sainsbury, 1982; Clavier and Richard, 1986; McGarvey et al., in press). However, logistical and cost constraints mean that survey regions typically cover smaller spatial scales than the fishery-management zones across which quotas must often be set. Therefore, quota-setting may rely on fishery-independent information from a small component of the available fishing grounds, together with catch-and-effort data for the management zone as a whole.
Here, we describe a method for determining the spatial distribution and harvestable biomass of a benthic invertebrate species that permits quota-setting across a management zone. The method was applied to a largely unexploited metapopulation of greenlip abalone (Haliotis laevigata) in northwestern Spencer Gulf, South Australia (Morgan and Shepherd, 2006), to assess the potential for these stocks to support commercial exploitation. The objectives of the study were, first, to determine the distribution and relative abundance of greenlip abalone on an "unexploited" reef system, second, to estimate the harvestable biomass in a subregion where greenlip abalone occur at commercial densities, and finally, to provide fishery managers with a risk-based framework within which to allocate quotas.
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Material and methods |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Study site and sampling programme
The study area was located off Cowell in the Central Zone of the South Australian abalone fishery (Figure 1), a fishery based on greenlip and blacklip (H. rubra) abalone, and managed using a range of input (e.g. limited to six licence holders) and output (e.g. species-specific quotas, size limits) controls (Nobes et al., 2004). Greenlip abalone make up >85% (143 t whole weight) of the current total allowable commercial catch in the Central Zone (167 t).
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The catch off Cowell, hereafter referred to as abalone, has historically been small, because a larger, consistently productive abalone reef (Tiparra Reef, eastern Spencer Gulf) is located much closer to one of the traditional launching points of the fishery. No catch was reported from Cowell until 1989, when
18 t whole weight was harvested in an experimental "fish-down". Catches from, and effort in, the area have been both low (<500 kg year–1) and sporadic since 1990, relative to regularly fished areas in the Central Zone. The study integrated two levels of spatial survey, the first providing essential information for the second. The two components were (i) directed, systematic, commercial surveys, and (ii) fishery-independent, research surveys.
Commercial surveys to determine abalone distribution and relative abundance
Directed, controlled surveys by commercial abalone fishers were undertaken in May 2004 (broad scale) and June 2005 (broad and fine scale). For the broad-scale survey, 110 km of coastline off Cowell was divided into seven regions (Regions A–G; Figure 1). Region F was the only offshore area surveyed because anecdotal information provided by a current commercial abalone fisher suggested the presence of reef there. Each of the seven regions was further subdivided into blocks 1 km long and wide (after Groeneveld and Cockcroft, 1997; n = 1143), from the shore seawards to 20-m depth contour. For the fine-scale survey, the focus and spatial resolution of exploratory fishing was increased in the second year by subdividing a subset of the blocks within Regions C and D (n = 95), which spanned the area of greatest abalone abundance, into blocks 0.5 km long and wide (i.e. 1/4 km2; n = 380; Figure 2).
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At both scales, GPS positions of the centre and corner points of each block were provided to commercial abalone fishers before the survey. Each fisher was designated a series of survey blocks to ensure systematic searching of the area. The substratum of each block searched was determined using a colour echosounder. Blocks in which the substratum was sand or seagrass, and where abalone were absent, were recorded. In blocks with suitable reef habitat, fishers entered the water and harvested all abalone that exceeded the size limit for the commercial survey (120-mm shell length, SL). Diving within each block was restricted to 1 h, during which a propelled dive cage was used by fishers to search for and to harvest abalone. Detailed catch-and-effort data were recorded for each block. This included the location (GPS position) at the start and end of each dive, and the number and weight of abalone harvested. Catch rate or catch per unit effort (cpue, kg h–1), calculated for each block as the whole weight harvested divided by the dive time, was used as a relative measure of abalone abundance.
Contour maps of abalone distribution and relative abundance were constructed from the fine-scale checkerboard grid of spatially resolved abundance measures, alternating blocks with a cpue value, using the radial-basis function in ArcView Spatial Analyst (version 8.3). Four cpue categories were used: high (>80 kg h–1), medium (20–80 kg h–1), low (1–20 kg h–1), and very low (<1 kg h–1).
Research surveys to estimate absolute density and harvestable biomass
Research surveys employed the leaded-line survey method (McGarvey, 2006; McGarvey et al., in press). This provides estimates of "absolute" abalone density in bounded survey regions, from which estimates of harvestable biomass can be obtained by multiplying survey-estimated absolute, harvestable, biomass density by the area of the survey region. To estimate abalone biomass off Cowell in 2005, the information on abalone distribution obtained from the commercial surveys was used in combination with the leaded-line survey method.
The broad-scale surveys by commercial fishers in 2004 and 2005 identified one principal area with abalone at harvestable densities. The contour maps of abalone distribution and relative abundance constructed from the cpue data, obtained from the finer-scale commercial surveys across this area in 2005, were used to delineate the boundary of a survey subregion that encompassed the 16.9-km2 area within which the cpue was high or medium (Figure 3). This also corresponded to the area from which most of the catch (>95%) had been harvested. Areas of low and very low cpue were largely excluded: these coincided with the area from which <5% of the catch was harvested.
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To improve the precision of the estimates of abalone density, and hence the harvestable biomass, the survey subregion was also stratified using the cpue contours, and the surveys targeted the strata of greater abalone abundance. To achieve this, 19, 12, and 1 leaded-line survey transect(s), respectively, were allocated into strata within which the cpue was high (5.6 transects km–2), medium (2.5 transects km–2), and low (7.7 transects km–2), respectively. These transects were allocated systematically inside each stratum, and all were laid in a north–south direction.
Two divers independently counted and measured the lengths (maximum SL) of all abalone observed within each of the 64 transects, defined as the area 1 m to either side of the 100-m leaded-lines. The count of abalone measured in each 100-m2 transect provided an estimate of density, and the length measurements provided a representative sample of the size frequency distribution of the population. Harvestable biomass in each stratum was estimated as the mean from these measures of legal-sized biomass density observed in each transect. Abalone numbers, by length, were combined with a derived relationship of SL to "bled meat weight (BMW)", to calculate biomass density by transect.
Harvestable biomass was quantified in units of BMW because "quota" in this fishery is defined in this unit. This survey measure accounts for commercial, post-harvest weight loss of abalone before "weigh in" at the processor (Gorfine, 2001). Data on the relationship between SL (mm) and BMW (g) were obtained from the commercial catch in June 2005. An allometric curve for mean BMW (BMW = a SLb) was fitted by maximizing a normal likelihood, with varying standard deviation, to estimate the weight–length parameters a and b. The model-fit residuals increased with SL, and the likelihood standard deviation was thus modelled by the power function 
SL = 0 BMW 1. Thus, BMW was given as a function of SL: BMW = 0.0000481 SL3.
Legal-sized population number and biomass in each stratum were calculated as the mean number or biomass density (g m–2) multiplied by the area of each stratum. Population size and harvestable biomass in the survey subregion were calculated as the sum of the totals from all three strata.
A stratified, three-level bootstrap (n = 10 000 iterations of resample with replacement) was used to determine the confidence range around the estimate of the harvestable biomass of abalone within each stratum. The three levels were (i) the loop over all strata, (ii) resamples from among leaded-line sample locations in each stratum, and (iii) resamples between the two transects at each leaded-line sample location (Appendix B of McGarvey et al., in press). The 10 000 bootstrap iterations of harvestable biomass were ranked, and the 10, 20, ..., 90% quantile confidence intervals (CIs) extracted. Decision tables (Punt and Hilborn, 1997) based on nine levels of lower-bound, survey-estimated biomass in the survey subregion (McGarvey et al., in press), each corresponding to a bootstrap confidence probability from 10% to 90%, provided a risk-assessment framework for quota decision-making.
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Results |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Abalone distribution and relative abundance
The broad-scale commercial surveys were undertaken in 595 (52%) of the 1143 1-km2 blocks off Cowell (Figure 1; Table 1), in >67% of the blocks in Region D, but less in the other regions (range 6–63%). In all, 8390 abalone of total whole weight of 3.1 t were harvested (Table 1). Most of the catch was made in Regions B (0.6 t; 20% by number and by weight) and D (2.1 t; 67% by number and by weight), few abalone were taken in Regions A (0.1 t), C (0.3 t), and E (0.1 t), and no abalone were observed in, or harvested from, Regions F and G. Large catches (>200 individuals) were only obtained from 16 1-km2 blocks (Figure 1), of which 13 were in Region D, all in an area stretching E–W that was
2 km wide by 16 km long. On average, 54 kg whole weight (18 kg BMW) was harvested on each fishing day. Mean cpue was 39 kg h–1 (Table 1), but it varied substantially among regions. It was high in Regions B (39 kg h–1) and D (60 kg h–1), low in Regions A, C, and E (<20 kg h–1), and zero in Regions F and G.
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During the fine-scale surveys, 174 (46%) of the 380 1/4-km2 blocks were surveyed, from which 6265 abalone of whole weight 2.5 t were harvested (Figure 2; Table 1). Most of the catch was in Region D (2.5 t; 99% by number and by weight), and large catches (>150 abalone) were obtained from 18x1/4-km2 blocks. Collectively, this accounted for 69% of the total catch (Figure 2). On average, 123 kg whole weight (
41 kg BMW) was harvested on each fishing day. Mean cpue was 67 kg h–1, and it was substantially greater in Region D (73 kg h–1) than in Region C (4.9 kg h–1; Table 1).
Estimates of absolute density and harvestable biomass
In all, 414 abalone (size range 45–172 mm SL) were counted and measured on the 64 transect lines. No abalone were observed on 30 lines (47%). The mean estimates of legal-sized density in the three strata were 0.058, 0.014, and 0.005 abalone m–2, respectively. The stratified mean estimate of total abalone density in the survey subregion was 0.047 abalone m–2.
The stratified mean estimate of the total number of abalone in the survey subregion was 800 000, of which 530 000 were of legal size (i.e. 
130 mm SL). The estimate of the total number agrees closely with the more approximate estimate obtained in 2004 (1 million ±40%) from a larger, surrounding area of 42 km2 (Dixon et al., 2004). A large percentage of the total (78%) and sublegal (85%) abalone were observed on transects located in the high-cpue stratum which covered less than half (40%; 6.8 km2) the survey subregion. The stratified, mean estimate of legal-sized, BMW biomass density in the survey subregion was 4.6 ± 1.5 g m–2.
The bootstrap outputs yielded survey biomass values for each risk level (10, 20, ..., 90%) probability that the true value of harvestable biomass is equal to or greater than each selected risk level biomass (Table 2; top row, parentheses). For the survey subregion of 16 876 500 m2, the estimated mean (±s.e.) harvestable biomass was 77 ± 25 t. The confidence ratio (s.e./mean) was 32.5%. There was a 90% probability that harvestable biomass exceeds 46.3 t, but only a 10% probability that it was >110.5 t (Table 2; top row, parentheses).
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Quota allocation in a new management subzone
Consideration of the decision table by State fishery managers and licence-holders in the fishery led to selection of a 10% harvest fraction with an 80% confidence of harvestable biomass exceeding 55.26 t. This resulted in a quota allocation of 5.5 t (BMW) within a new management subzone, representing an increase of 11% in the total allowable commercial catch for abalone in the Central Zone.
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Discussion |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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The integrated, two-stage, survey approach applied at Cowell permitted the systematic mapping of distribution and estimation of harvestable biomass across a large area in the absence of prior knowledge of abalone distribution and abundance. The survey outputs, presented as a decision table and used with a set of decision rules, led to allocation of an additional quota inside a new management subzone, which added considerably to that of the Central Zone abalone fishery. The successful outcome depended on combining commercial and research surveys to provide reliable estimates of harvestable biomass within a risk-analysis framework. It was underpinned by robust partnerships and cooperation among all stakeholders—research agencies, management authorities, and industry—which were integral to its success (Perry et al., 2002; Haapasaari et al., 2007; van Densen and McCay, 2007).
Using commercial fishers to undertake directed fishing surveys within designated areas for specified periods of time capitalized on their targeting behaviour (Ellis and Wang, 2007), and proved effective in providing data on the broad-scale distribution and abundance of abalone over a large area (1143 km2 distributed along 110 km of coastline) in a short period (<2 weeks spread between 2004 and 2005). The fact that a large proportion of the catch was obtained from Region D, allied to the absence of abalone in Regions F and G, demonstrates that abalone are not distributed widely off Cowell, but that there is principally one substantial aggregation in a band (2–3 km wide by 16 km long) running E–W between 3 and 8 km south of the coast in Region D. Even within this area, abalone were not ubiquitous: catches during the fine-scale commercial surveys in 2005, which encompassed the extent of this aggregation, were highly variable. Most blocks yielded no abalone (73%), and large catches, which cumulatively accounted for 69% of the catch, were obtained from just 18 of the 174 blocks surveyed. Therefore, the commercial surveys efficiently explored large areas to identify regions with high densities of abalone suitable for harvesting.
GIS-based mapping of fishery-dependent data derived from the commercial surveys (see Chigbu et al., 2006) ensured that the research survey targeted the areas of high cpue. Directing the research survey at bounded and stratified subregions (Ault et al., 1999) permitted substantially higher precision of the survey estimates than would have been possible in the absence of these data (Ault et al., 1999; von Szalay, 2003; Dressel and Norcross, 2005). Therefore, this approach reduces sample variance and survey cost, overcoming the problems of low precision and expense associated with such exercises (Walters and Pearse, 1996; McShane, 1998).
Substantial advantage was also obtained by selecting a research-survey method that measured "absolute" population density (Woodby et al., 1993; McGarvey, 2006; McGarvey et al., in press). Notably, estimates of absolute, harvestable biomass provide both knowledge of absolute population size before commercial fishing, thereby benchmarking the resource being exploited, and, perhaps more important, enabled a quota, with quantifiable risk, to be set from a single survey, using harvest strategies based on a set of decision rules. Measuring absolute density reliably facilitates the rapid implementation of a regulated fishery, with minimal risk of overexploitation, or overinvestment, or both of these factors together, and provides increased confidence for fishery managers under pressure to approve such new fisheries (Perry et al., 1999). Further, this approach removes the need to exploit the stock to determine its size, and also alleviates the need for further, more assumption-driven stock-assessment modelling, by which absolute population size in fisheries is typically inferred (Hobday and Punt, 2001; Breen et al., 2003; Gorfine et al., 2005).
Our survey design, in which the transect area searched by divers was carefully controlled and the locations of leaded-line transects inside each stratum were systematic, ensured that the estimates of absolute density, population size, and harvestable biomass were representative of the survey subregion and were unbiased. This was achieved in a survey subregion considerably larger than any other known survey on abalone (e.g. Sainsbury, 1982; Cripps and Campbell, 1998) or other benthic invertebrate (e.g. Schweizer and Posada, 2002). To our knowledge, it was also the first to estimate the absolute harvestable biomass in a stratified survey subregion.
No abalone were observed in a large proportion of the transects, suggesting that they are patchily distributed throughout the area. Nevertheless, survey estimates of abalone density, absolute abundance, and harvestable biomass for the 16.9-km2 survey subregion were high. For example, estimates of the total number of legal-sized abalone and the mean harvestable biomass were 530 000 individuals and 77 t (meat weight), respectively.
The high cpue in Region D observed during the commercial surveys, coupled with the large estimate of harvestable biomass obtained from the research surveys, suggested the potential for the abalone stocks off Cowell to support a commercially viable fishery—separate from that already established elsewhere in the Central Zone. The survey outputs were presented as a decision table (Table 2). Recognizing the need to adopt a precautionary approach—stemming from the high risk of abalone fisheries collapsing (Davis et al., 1996; Haaker et al., 1996; Tegner, 2000; Hobday et al., 2001)—this decision table was used with a set of decision rules, chosen through consultation between managers and industry, to set a quota inside a new management subzone. A conservative harvest strategy was selected, viz. 10% harvest fraction with an 80% confidence of harvestable biomass exceeding 55.26 t (Table 2), and this additional quota added considerable value to the Central Zone abalone fishery. Development of new management areas with an allocation by State fishery managers of an additional quota and a manager-specified risk of overestimating biomass is rare for abalone fisheries, because most are either fully or overexploited (Prince, 2005).
The increase in quota (5.5 t meat weight, 16.6 t whole weight) added significant value to the abalone fishery (10%, A$550 000), providing a direct and measurable return on investment. This outcome is also notable for the short time (87 d) and relatively low cost (A$200 000) of the exercise by means of which biomass estimates, with useable precision, were provided for such a large area. The quota was harvested during May 2006 and obtained in 34 fishing days (Mayfield et al., 2006a). The commercial catch rates (90 kg h–1) were similar to those on the most productive greenlip abalone reefs (range 60–99 kg h–1) in the South Australian abalone fishery over the past 5 years (Chick et al., 2006; Mayfield et al., 2006b), confirming the potential for the area to support a separate quota.
The development and application of this method has provided substantial and immediate benefits to the Central Zone abalone fishery, and suggest that our two-stage (industry-research), collaborative approach constitutes a useful tool for broader fisheries management in that it conveys many advantages for the assessment and management of invertebrate dive fisheries. Key among these are: first, utilizing the ability of commercial fishers to identify regions where the target species occurs at densities suitable for harvesting; second, the use of spatially systematic data from exploratory fishing by commercial fishers to identify and stratify bounded survey subregions for research surveys; and third, employment of a survey method that returns a measure of absolute population density and harvestable biomass from which a quota, with quantified risk, can be set. Not only does this approach allow the rapid implementation of a regulated fishery, with controlled risks of overexploitation and overinvestment or of either factor alone, it also permits quota-setting across management zones in developing or fully developed benthic invertebrate fisheries. Further, it provides a mechanism to achieve a reduction in the spatial scale of invertebrate fisheries management, and this more closely aligns the scales of management and stocks. The need for this to happen has been growing stronger in recent years (Prince and Hilborn, 1998; Prince, 2005; Naylor et al., 2006).
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Acknowledgements |
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Funding for this project was provided through licence fees obtained from commercial abalone licence-holders in the Central Zone abalone fishery. The licence-holders, commercial fishers, and deckhands are thanked for their encouragement and cooperation in conducting the exploratory fishing. We are grateful to Rowan Chick, Brian Foureur, Thor Saunders, Matthew Goggoll, Coby Matthews, Shane Penny, and Nick Turich for diving, fieldwork, and logistical support, and to Annette Doonan for creating the maps and GIS information, and to John Feenstra for the interpolations on cpue. We also thank Chris Royans (Hot Dog Fisheries) and David Pickles (Dover Fisheries) for collecting the catch-weight data. Rowan Chick, Adrian Linnane, Craig Noell, Bob Pennington, Paul Rogers, Scoresby Shepherd, Sean Sloan, Martin Smallridge, and Michael Tokley provided useful comments on early drafts, and two anonymous referees also gave valuable input that substantially improved the paper.
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References |
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