Enhancing fishery assessments for an Australian abalone fishery using commercial weight-grade data

Fisheries Research 105 (2010) 28–37

Contents lists available at ScienceDirect

Fisheries Research journal homepage: www.elsevier.com/locate/fishres

Enhancing fishery assessments for an Australian abalone fishery using commercial weight-grade data S. Mayfield ∗ South Australian Research and Development Institute, PO Box 120, Henley Beach, SA, 2022, Australia

a r t i c l e

i n f o

Article history: Received 22 October 2009 Received in revised form 17 February 2010 Accepted 22 February 2010 Keywords: Catch sampling Commercial fishery Performance measure

a b s t r a c t Effective management of commercial fisheries is dependent on the reliability of fishery assessments that, in turn, are determined by the quantity and quality of the available data. Data on the length-structure of the catch provide important information on exploited stocks, but representative sub-sampling is difficult to achieve, especially where numerous licence holders fish over large areas. This study evaluates the potential for routinely collected, commercial, weight-grade data to overcome this challenge and enhance assessment of the Western Zone blacklip (Haliotis rubra) and greenlip (Haliotis laevigata) abalone fisheries in South Australia. Current weight-grade data for blacklip abalone are inaccurate. However, the accuracy, rigour and consistency of the data for greenlip abalone confirm they are sufficiently reliable for aiding fishery assessments on this species. The greenlip abalone data were highly representative of the catch and the fishery and have been obtained in a consistent manner for >20 years. Changes in the composition of the grades in the catch provide a meaningful measure of change in the harvested stock. Similarly, measures of mean weight and number harvested, derived from the weight-grade data, are valuable as additional indices of stock status. These attributes substantially enhance the credibility of these data for use in assessing changes in stock status through time, and suggest these data should be used to supplement current assessments of this species. The value of the weight-grade data would increase substantially if the resolution was improved by using automated weighing and grading systems. The analysis of weight-grade data, routinely collected from numerous fisheries, is likely to benefit their assessment. © 2010 Elsevier B.V. All rights reserved.

1. Introduction The primary purpose of fisheries management is to ensure that harvests are ecologically sustainable. This objective has been difficult to achieve (Frank and Brickman, 2001; McWhinnie, 2009) and, consequently, many fisheries are in decline (Myers and Worm, 2003; Mora et al., 2009). Failure to manage fisheries effectively can have substantial effects on social, economic and ecosystem conditions (Maunder et al., 2006; Worm et al., 2006, 2009) and has received considerable public interest in recent years (Hilborn et al., 2006; Beddington et al., 2007). Management of commercial fisheries is dependent on the reliability of fishery assessments (Hilborn and Walters, 1992; Rice, 1999), that, in turn, is dependent on the quantity and quality of the available data (Hilborn, 1997; Chen et al., 2003; Scheirer et al., 2004). A broad range of sampling approaches are utilised to obtain the diverse datasets used in contemporary fishery assessment (Heales et al., 2007). Collection and analysis of data on the length- or age-structure of the catch comprises one of the fun-

∗ Tel.: +61 8 8207 5427; fax: +61 8 8207 5406. E-mail address: Stephen.Mayfi[email protected]. 0165-7836/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fishres.2010.02.007

damental sources of information on the dynamics of exploited stocks (Gerritsen and McGrath, 2007; Miller and Skalski, 2006). While these data are used to fit integrated stock assessment models (Punt and Kennedy, 1997; Haddon, 2000; Punt, 2003), they have a diverse range of uses including estimating recruitment, exploitation, growth and mortality rates (Fournier and Breen, 1983; Andrew et al., 1997; Oh et al., 1999) and as direct measures of changes in the length-composition of the catch (Andrew and Chen, 1997; Bianchi et al., 2000). Data from catch sampling are generally acquired at sea by onboard observers (DeMartini et al., 2000), or at processing plants and fish markets (Fowler et al., 2008). However, a key limitation of these approaches is the necessity to sub-sample from the commercial catch. A robust, ‘fit-for-purpose’ sampling design, including determination of required sample sizes, is essential to ensure an appropriate and representative sampling program (Andrew and Chen, 1997; Gerritsen and McGrath, 2007). The consequences of an inappropriate sampling program include potentially uncertain, biased and erroneous fishery assessments (Chen et al., 2003; Scheirer et al., 2004; Heery and Berkson, 2009). Adequate, representative catch sampling is especially difficult to achieve in those fisheries that comprise numerous licence holders fishing over large spatial scales, and/or where there is high variability among catches.

S. Mayfield / Fisheries Research 105 (2010) 28–37

29

Fig. 1. Map showing the location of FA 4, 8, 9, 14 and 18 in Region A (WZ A) of the Western Zone of the South Australian abalone fishery. CZ and SZ refer to the Central and Southern Zones, respectively.

Furthermore, fishery-dependent sampling programs dedicated to addressing these issues typically suffer from low participation levels, limited compliance with the sampling requirements, weak correlation with similar data obtained by scientific observers and practicality (Collins, 1986; Rotherham et al., 2007). Abalone (family Haliotidae, genus Haliotis) are gastropod molluscs that support valuable fisheries in many parts of the world (Hamasaki and Kitada, 2008). These fisheries typically feature a high degree of variability among catches (Saunders et al., 2008), and have numerous licenced fishers harvesting abalone over large spatial scales (Prince and Shepherd, 1992). Under these circumstances, obtaining samples that are representative of the commercial catch is difficult. This is certainly the case in the Western Zone (WZ) of the South Australian abalone fishery, where data from commercial catch sampling are typically poorly representative of the catch and the fishery (Chick et al., 2009). For example, in 2008, data were available from only 5 of 23 licence holders in the fishery, that collectively provided <6000 shell-length measurements from a total catch of ∼600 t. The problem with obtaining representative catch-sampling data stems from this fishery being unusual in that licence holders are permitted to ‘shuck’ abalone at sea (i.e. separate the muscular foot from the shell and viscera, which are discarded). As just the abalone ‘meats’ are landed, there is little incentive to retain shells for commercial catch sampling. However, abalone meats harvested from the WZ are routinely graded, by weight, at the processing factories prior to being packaged and sold. While weight-grade data have been used in assessment of prawn (Dixon et al., 2009; Roberts et al., 2009) and considered for lobster fisheries (Walker and Bentley, 2002), their direct utilisation is rare – despite the probable rou-

tine collection of this information by processors for many fisheries. Thus, significant advantage could be obtained if the weight-grade data from the processors represented a suitable surrogate for the catch-length-frequency data and/or were suitable to aid assessment of fished stocks, especially in the absence of additional cost. This would diversify and increase the quantity of data available, which is important in enhancing the quality, reliability and accuracy of fishery assessments (Scheirer et al., 2004). The aim of this study is to evaluate the potential of the weightgrade data to aid assessment for the WZ abalone fishery. The primary objectives were to (1) assess the quality of the data; (2) identify direct and derived indices of stock status to supplement current assessments; and (3) determine whether they could replace the catch-length-frequency data as a measure of the lengthstructure of the commercial catch. 2. Methods 2.1. Description of the fishery The South Australian abalone fishery (SAAF) began in the early 1960s, with management arrangements evolving since its inception. These include sub-division of the fishery into three zones (Western, Central and Southern) in 1971 (Nobes et al., 2004; Fig. 1). The WZ has 23 licence holders and was further subdivided into Regions A and B in 1985 (Fig. 1). Annual, total allowable commercial catches (TACCs) were introduced for greenlip (Haliotis laevigata) and blacklip (Haliotis rubra) abalone (hereafter referred to as greenlip and blacklip, respectively) in Region A in 1985 (Nobes et al., 2004). Quotas are issued and decremented in meat weight. The

30

S. Mayfield / Fisheries Research 105 (2010) 28–37

Table 1 Commercial abalone meat-weight-grade categories (g) used by the principal processing factories in the WZ. The values used in this study are also shown. Abalone grade

Western Abalone Processors

Abalone Down Under

Streaky Bay Marine Products

Default for this study

Grade 1 (large) Grade 2 (medium) Grade 3 (small)

≥230 160–229 <160

≥225 150–224 <150

≥230 150–229 <150

≥230 150–229 <150

greenlip TACC in Region A was 69 t meat weight from 1989 to 2005 and 75.9 t meat weight (∼227 t whole weight) from 2006. The TACC for blacklip has been 97.75 t meat weight (∼293.25 t whole weight) since 1989. Minimum legal lengths (MLL) of 130 and 145 mm shell length (SL) apply for blacklip and greenlip, respectively. There are also minimum meat weights (greenlip: 140 g; blacklip: 113 g). Both species can be harvested simultaneously. Current scientific assessments are based on data from a range of sources. Data include those provided by commercial fishers (e.g. catch, effort and catch-sampling data) and those collected by researchers (e.g. biological and fishery-independent survey data; see Chick et al., 2009). Stock status is determined by evaluating these data separately and collectively, but the data are not integrated by an ageor length-structured model. Fishery performance is also evaluated through a range of prescribed performance indicators (Nobes et al., 2004). 2.2. Data sources Three sources of data were used to obtain the information and undertake the analyses in this study. They were: (1) commercial catch, effort and weight-grade (2) commercial catch sampling, and (3) fishery-independent, morphological data. Commercial catch and effort data for the abalone fishery in Region A have been collected since 1968. One major change was made to the data-collection system in 1978, when the sub zones and fishing blocks were replaced with smaller spatial units termed fishing areas (FA; numbered 3–20; see Fig. 1). FA are further subdivided into a series of map codes (e.g. 9A, 18F). Research logbooks are completed for each fishing day and submitted to the South Australian Research and Development Institute (SARDI) monthly. The logbook provides information on the date of fishing, the map code and the total catch landed. Most logbooks also provide the weight of each abalone grade (Grade 1 – large; Grade 2 – medium; and Grade 3 – small; see Table 1) comprising that total catch. Commercial-catch, length-frequency data are available from Region A for both greenlip (1979–1982 and 1999–2008) and blacklip (1999–2008; see Chick et al., 2009). Prior to 1 July 2005, these data were obtained from samples provided to SARDI from commercial fishers. Since July 2005 the Abalone Industry Association of South Australia Inc. has provided these data, which are obtained by the commercial fishers, to SARDI. Information on the date of fishing, map code, licence number and species are recorded for each set of individual shell lengths (SL, mm). Morphological data for greenlip were collected by SARDI from Flinders Island (FA 9) and Searcy Bay (FA 4; November 1999), ‘Hotspot’ (FA 9; May 1999), Price Island (FA 15; June 1999) and ‘The Gap’ (FA 18; June 1999 and January 2000). Data collected included sampling date, species, sampling site, the sex, SL, whole weight (g), meat weight (g) and 24-h bled-meat weight (BMW, g) for each abalone sampled. The latter measurement reflects the “bleeding” of abalone meats during the typical time period between shucking at sea and subsequent processing, including “weighing off”, undertaken at the processing factories (Gorfine, 2001). This measure was obtained using the same methods as the commercial fishers apply to their daily catches.

2.3. Quality of the weight-grade data Four abalone processing factories collectively process >95% of the abalone harvested from Region A. Three of these account for the catch from ∼22 of the 23 licence holders (∼96%). Information on the protocols and procedures for processing the abalone catches, including the weights defining the three weight-grade categories, was obtained by interviewing processors either during site visits conducted in April 2009 or by telephone in May 2009. Site visits included observations of catch processing. The information provided by the processors was used to evaluate the quality of the weight-grade data and to undertake the analyses on greenlip described below. Similar analyses were not undertaken for blacklip due to the lack of accurate weight-grade data for that species. The logbook data supplied by the commercial fishers were used to determine the degree to which the weight-grade data were representative of both the catch and the fishery in Region A. To do that, the percentage of the daily and total catches (for which weightgrade data were available) and the number of commercial fishers providing these data, were calculated. The percentage of the daily catches for which weight-grade data were available for greenlip was only determined from those daily records where the catch of greenlip on that day was not reported as zero. The percentages of weight-grade data for the daily and total catches were highly correlated (r > 0.99). Only total catch data are presented. 2.4. Use of the weight-grade data to supplement fishery assessment Two approaches were explored to evaluate the potential for the weight-grade data to aid current assessment of the fishery. First, temporal patterns in the weight-grade data were examined to determine the level of inter-annual variability and the degree to which temporal changes could be interpreted as meaningful trends for the fishery. The logbook data supplied by the commercial fishers were used to determine the spatial and temporal patterns in the greenlip weight-grade data from 1979 to 2008. This was achieved by calculating the percentage contributed by each of the three weight-grade categories to their combined weight. Temporal trends were determined for the whole of Region A and four key FA (8, 9, 14 and 18; see Chick et al., 2009). Secondly, use of the weight-grade data to estimate the number of greenlip harvested and their minimum mean weights were examined. The current low resolution of the weight-grade data limited these estimates to the maximum number and minimum mean weight of greenlip harvested. The estimates were derived from data on the proportions of each grade of greenlip in the commercial catch that was obtained from the logbooks. This required four steps: (1) the percentage contributed by each of the three weight-grade categories to their combined weight was calculated; (2) the proportion of each weight grade was multiplied by the total catch (graded and ungraded) to provide an estimate of the total weight harvested within each weight-grade category; (3) the weight harvested within each grade category was divided by the minimum meat-weight limit for that grade (large: 230 g; medium: 150 g; small: 140 g; see Table 1); and (4) the number of abalone harvested within each grade category was summed. Minimum mean weight was estimated by dividing the total catch by the estimated

S. Mayfield / Fisheries Research 105 (2010) 28–37

31

maximum number of abalone harvested. Temporal trends in the numbers of abalone harvested, and their minimum mean weight, were determined for the whole of Region A and FA 9 and 18. 2.5. Relationships between weight-grade and length-frequency data Relationships between weight-grade and length-frequency data were determined by pairing data on the proportions of each grade of greenlip in the commercial catch obtained from the logbooks, with commercial length-frequency data that were obtained on the same fishing day, using three unique identifiers (date, licence number and fishing location). The percentage contributed by each of the three weight-grade categories to their combined weight was determined for each paired sample. Thereafter, the mean SL for each paired sample was determined from the length-frequency data. The strengths of the relationships between the percentage of Grade 1 greenlip in the commercial catch and mean SL were determined using coefficients of determination (r2 ). These analyses were undertaken for all paired samples in Region A from 2004 to 2008 and for each year from 2005 to 2008 separately. The relationship between the percentage of Grade 3 greenlip in the commercial catch and mean SL in Region A was also determined using data from 2004 to 2008. The numbers of paired data sets at finer spatial and temporal scales were limited. However, data from FA 9 and mapcode 9A during summer 2007 were used to evaluate the effect of fishing location and season on these relationships. Relationships between SL and BMW were evaluated using fishery-independent, morphological data. Limitations in the data available required that data from all sites and sampling times were used to determine relationships between SL and BMW for male and female greenlip. Subsequently, as there was no significant difference in this relationship between males and females (Z = 0.85, P > 0.05) data from Hotspot, Price Island and The Gap, collected within a 2-week period in autumn 1999, were used to evaluate the effect of sampling location on the SL-BMW relationship. Relationships were evaluated using r2 values. Pearson’s correlation coefficients (r) were compared among sampling locations using a 2 test following transformation of r to a z value (after Zar, 1984). Distributions of potential BMW values within seven 5-mm shell-length classes (145–149; 150–154; . . . >175 mm SL) were determined from the same morphological data. Potential SL distributions from greenlip meats in each of the three weight-grade categories (large: ≥230 g; medium: 150–229 g; small: 140–149 g; see Table 1) were similarly derived. The median, maximum, minimum, upper 75th and lower 25th quantiles of each distribution were extracted. 3. Results 3.1. Quality of the weight-grade data Discussions with representatives from three of the four processing factories in the WZ identified subtle differences among the factories’ processing procedures, and distinct differences between the methods by which weight-grade data were obtained for greenlip and blacklip. Although each processor uses three meat-weight grades, each factory used slightly different weight categories for each species (Table 1). The greatest difference among factories was 10 g. Greenlip and blacklip are processed differently. For greenlip, at each of the three factories, the whole catch from each licence holder is graded separately with every individual greenlip assigned a grade. The different grades are then weighed separately. This provides a reliable estimate of the distribution of each daily catch of

Fig. 2. (Top) Percentage (bars) of the total catch of greenlip and number of licences (closed circles; maximum = 23) for which commercial weight-grade data were available between 1979 and 2008; (bottom) mean percentage of the total catch from 1979 to 2008 for which commercial weight-grade data were available for Region A (A) and each of the 18 fishing areas comprising the Western Zone.

greenlip, from each licence holder, into each grade. However, for blacklip, the same level of rigour is seldom applied. At one processor, the whole daily blacklip catch from each licence holder is weighed. Thereafter, coarse estimates of the proportions of that catch that would fall into the three grade categories are made visually, through cursory examination of the catch in a crate. The weight of blacklip in each grade is then determined by multiplying the weight of the total catch by the estimated proportion of that catch in each grade. At the remaining two processing plants, the blacklip catches are not graded, with the exception of the small (<2%) amount that is sold frozen, rather than canned; those blacklip sold frozen are graded to the standards that are applied to greenlip. Thus, estimates of the distribution of each daily catch of blacklip, from each licence holder, into each grade are much less reliable than those for greenlip and, consequently, all subsequent data and analyses relate to greenlip only. Weight-grade data were available for greenlip from 1979 to 2008, with the exception of 1997, 1998 and 1999 (Fig. 2). Since 1979, these data were available for ∼70% of the total catch (range: 0% in 1997, 1998 and 1999 to 95.9% in 1981). Notably, the percentage of the total catch for which weight-grade data were available has declined from ∼80% between 1979 and 1996 to ∼70% since 2001. On average, data were available from ∼75% of the 23 licence holders. However, the number of licence holders for which data were available was lower in the period from 2001 to 2008 (mean: 18.3), when compared to that from 1979 to 1996 (mean: 20.4). Importantly, weight-grade data were available for a high percentage of the total catch (range: 57–79%) from each FA from 1979 to 2008 (Fig. 2), despite total greenlip catch from these areas varying substantially (range: 12–1660 t). This suggests that the greenlip weight-grade data are strongly representative of the catch and the

32

S. Mayfield / Fisheries Research 105 (2010) 28–37

Fig. 4. Total catch (bars), maximum number (closed circles) and minimum mean weight (open circles) of greenlip in Region A and in FA 9 and 18 between 1979 and 2008.

fishery, because they provide data on the structure of the catch across all fishing areas and a high proportion of licence holders. 3.2. Use of the weight-grade data to supplement fishery assessment

Fig. 3. Percentage of Grade 1 (closed circles), Grade 2 (open circles) and Grade 3 (closed squares) greenlip in the commercial catch from Region A and from FA 8, 9, 14 and 18 between 1979 and 2008.

Inter-annual variability in the percentage of Grade 1, 2 and 3 greenlip in the catch was generally low. However, strong temporal patterns were clearly evident. For example, the percentage of Grade 1 greenlip in the commercial catch from Region A has increased significantly since 1980 (linear regression (LR): r2 = 0.78; P < 0.01; Fig. 3). The percentages of both Grade 2 and Grade 3 greenlip have decreased substantially over the same time period. Grade 1 greenlip have comprised >50% of the commercial catch since 2000, whilst the percentage of Grade 3 greenlip has not exceeded 20%. In combination, these data suggest that (1) current catches are dominated by larger greenlip, (2) few Grade 3 greenlip are harvested each year, and (3) the average weight of greenlip in the commercial catch has been gradually increasing over the past 30 years. In contrast to this general trend, the percentage of Grade 1 greenlip has declined since 2005. In 2008, it was 61.7%, the lowest since 2004 (58.5%). This reduction probably reflects an increase in fishing pressure brought about by the increase in the TACC from 2006. Strong temporal patterns were also evident at a smaller spatial scale (Fig. 3). In FA 9, for example, the percentage of Grade 1 greenlip increased significantly from 1982 to 2006 (LR: r2 = 0.58; P < 0.01)

S. Mayfield / Fisheries Research 105 (2010) 28–37

before decreasing sharply between 2006 (65.0%) and 2008 (41.7%) to the lowest level since 1993. Temporal patterns in FA 8 and 14 were less clear. However, in FA 14, similar proportions of Grade 1 and Grade 2 greenlip were harvested between 1979 and 1996. Since 2000, Grade 1 abalone have dominated the catch from this FA. The composition of the commercial catch has been comparatively stable in FA 18, with catches dominated by Grade 1 greenlip. Estimates of the maximum number of greenlip harvested, and their minimum mean weight, also showed strong temporal patterns. In Region A, the number of abalone harvested has reduced from >2.5 million to <1.3 million since the mid-1980s. Recent small increases probably reflect a combination of the increase in TACC from 2006 and the reduction in minimum mean weight of the catch (Fig. 4), that, in turn, was caused by decreases in the proportion of Grade 1 greenlip in the catch (Fig. 3). Recent temporal patterns in FA 9 and 18 were different from those for Region A. Notably, in FA 9, catch by number, catch by weight and the minimum mean weight

33

Table 2 Grade, r2 value, sample size (n) and P value from the relationships between paired samples of mean shell length and the proportions of graded greenlip in the commercial catch. NS indicates the relationship was not statistically significant. Location

Year

Grade

r2

n

P

Region A Region A Region A Region A Region A Region A FA 9 Map code 9A

2004–2008 2004–2008 2005 2006 2007 2008 Summer 2007 Summer 2007

Grade 1 Grade 3 Grade 1 Grade 1 Grade 1 Grade 1 Grade 1 Grade 1

0.20 0.04 0.04 0.29 0.14 0.25 0.01 0.28

154 154 19 28 63 13 27 8

<0.05 <0.05 NS <0.05 <0.05 NS NS NS

Fig. 5. Relationships between paired mean shell length (mm) and the proportion of Grade 1 greenlip in the commercial catch in Region A from 2004 to 2008 (combined) and for 2005, 2006, 2007 and 2008 separately, for FA 9 and map code 9A during summer 2007 (January–March). The relationship between paired mean shell length (mm) and the proportion of Grade 3 greenlip in the commercial catch from 2004 to 2008 (combined) from Region A is also shown.

34

S. Mayfield / Fisheries Research 105 (2010) 28–37 Table 3 Sex, r2 value, sample size (n) and P value from the relationships between shell length (mm) and bled-meat weight (g) for greenlip sampled from Region A. Sample site

Date

Sex

r2

Region A Region A Hotspot Price Island The Gap

May 1999–January 2000 May 1999–January 2000 May 1999 June 1999 June 1999

Male Female All All All

0.72 0.66 0.81 0.77 0.88

n 120 101 35 43 45

P <0.05 <0.05 <0.05 <0.05 <0.05

spread of data occurred despite generally strong, positive correlations between shell length and bled-meat weight for greenlip in Region A (Fig. 6; Table 3) with no clear separation between male and female greenlip, and no significant differences among sampling locations (2 = 3.47, P > 0.05). This problem also resulted in a broad range and considerable overlap of both (1) potential BMW values within narrow (5-mm) shell-length classes, and (2) potential greenlip lengths within each of the three weight-grade categories (Fig. 7). For example, each of the three grade categories could contain the meat from an individual abalone with a SL of 160 mm SL. Similarly, an individual BMW of 200 g could potentially have been obtained from an abalone of between 145 and 174 mm SL. 4. Discussion

Fig. 6. Relationships between shell length (mm) and meat weight (g) for male (open circles) and female (closed circles) greenlip (sites combined; top) and for all greenlip sampled from Hotspot (open circles), Price Island (closed circles) and The Gap (crosses; bottom) during autumn 1999.

of greenlip harvested have all decreased sharply to the lowest levels in ∼15 years. In contrast, the estimated mean weight of greenlip in FA 18 has been relatively stable.

There were three principal findings from the evaluation of the weight-grade data for the WZ abalone fishery. These were that (1) accurate weight-grade data were only available for greenlip, (2) greenlip weight-grade data provide valuable information for aiding assessments of stock status for this species in the WZ, and (3) use of weight-grade data to replace the catch-length-frequency data as a measure of the length-structure of the commercial catch is inappropriate. There were large differences in the quality of the weight-grade data between the two harvested species. A small volume of blacklip that is sold frozen (<2%) is graded accurately. However, about half the catch is only “graded” through the superficial, visual inspec-

3.3. Relationships between weight-grade and length-frequency data Although the slopes of several correlations between paired samples of mean shell length and the proportions of Grade 1 or Grade 3 greenlip in the commercial catch were significantly different from zero, the relationships were generally weak (Fig. 5; Table 2). The relationships between these two variables were also seldom improved when the two principal confounding factors, season and location, were taken into account. This problem extended to the shortest temporal and smallest spatial scales considered (e.g. map code 9A in summer 2007; Fig. 5). Notably, r2 values did not exceed 30% (Table 2). This indicates that there is little evidence of a relationship between mean shell length and the proportion of Grade 1 greenlip in the commercial catch. The lack of strong correlations between these datasets suggests that the weight-grade data are not an appropriate replacement for the length-frequency data. The absence of strong correlations between mean shell length and the proportion of Grade 1 greenlip in the commercial catch was likely caused by the large proportion of data distributed within a narrow meat-weight range (100–200 g) that spanned a broad range of shell lengths (145–175 mm SL; Fig. 6). Consequently, a single meat-weight value could be obtained from greenlip with a wide range of shell lengths and, similarly, a range of meat-weight values could be obtained from greenlip with the same shell length. This

Fig. 7. Box and whisker plots showing the distribution of (top) potential bled-meat values within 5 mm shell-length classes and (bottom) potential shell-length values from each of the three weight-grade categories. The whiskers show the minimum and maximum values of the data, the upper (75th) and lower (25th) quantiles of the data are shown as the top and bottom lines of the box and the median is dark line within the box.

S. Mayfield / Fisheries Research 105 (2010) 28–37

tion of the catch with the rest remaining ungraded. This approach means that the current blacklip weight-grade data are inaccurate. While the current and historical data should not be used for assessment of the blacklip fishery, the collection of more precise data could aid future assessments. In contrast, the accuracy, rigour and consistency with which the data for greenlip have been collected suggest that the greenlip weight-grade data are sufficiently reliable for use in aiding current fishery assessments for this species. Although there were trivial differences in the weight ranges of the same grade among the different processing factories, at each factory the whole greenlip catch from each licence holder is graded and then weighed separately. This methodology provides a reliable measure of the distribution of each daily catch of greenlip, from each licence holder, into each grade that is consistent among the processing factories. One of the principal strengths of the greenlip weight-grade data is the degree to which they are representative of the catch and the fishery. For example, data were available for ∼70% of the greenlip catch since 1979, and have been provided by a high proportion of licence holders in each year. Further, weight-grade data were available for a high percentage of the total catch from each FA, despite total greenlip catch from these areas varying substantially. Thus, the weight-grade data provide information on the structure of the catch, from a high proportion of licence holders, across the entire fishery and for all fishing areas individually. Importantly, as each catch is treated equally, these data are not biased by fishing location, timing of the fishing activity or licence holder. Consequently, these data are far more representative of the fishery and the catch than the current catch-length-frequency data (Chick et al., 2009). Obtaining information on the structure of the catch in fisheries occurring over large spatial scales with numerous licence holders is often cost prohibitive and logistically difficult, with appropriate sub-sampling of the commercial catch essential to ensure a representative sampling program (Andrew and Chen, 1997; Gerritsen and McGrath, 2007). This can be difficult to achieve, especially in abalone fisheries where there is commonly a high degree of variability among catches (Saunders and Mayfield, 2008) that stems from the fine-scale population structure common to these stocks (Prince, 2005; Prince et al., 2008; Saunders et al., 2008; Saunders and Mayfield, 2008). Use of the routinely collected weight-grade data overcomes this issue for potentially little, or no, additional cost. A second key strength of the weight-grade data is that they have been obtained in a consistent manner by all three processing factories for over two decades. The lack of changes to either the weight-grades or the data-collection method over this time period substantially enhances the credibility of these data for use in assessing changes in greenlip stock status through time. Indeed, the rarity of such long-term datasets considerably increases their value (Peuser and Kline, 1997; Casselman et al., 1999; Shears et al., 2006). The strong temporal patterns in the percentage of Grade 1, 2 and 3 abalone in the catch suggests that these could be interpreted with similar approaches to those used for length- or age-structure data. Estimates of the maximum number of greenlip harvested, and their minimum mean weight, were equally informative because they also showed substantial changes through time. While measures of mean weight have the potential to be highly informative (McGarvey and Matthews, 2001), and have been used to aid assessment of selected lobster and prawn stocks (Campbell, 1992; De Sousa et al., 2006; Linnane et al., 2009), their use is seemingly rare. In concert with total catch, estimates of the number of individuals harvested provide another, direct measure of resource extraction. This information is rare in most fisheries, including those for abalone. These estimates of mean weight and the total number of abalone harvested could significantly improve any future fishery assessments or numerical modelling in this fishery, and in others where such

35

weight-grade data are routinely available (McGarvey et al., 1997, 2005; McGarvey and Matthews, 2001). Fishing Area 9, one of the most productive FA in Region A, provides a good example of the value of these data to aiding fishery assessment. In this FA, the percentage of Grade 1 greenlip in the catch has decreased sharply since 2006. While it is possible that the harvest of a higher number of smaller greenlip reflects elevated recruitment to the fishable stock, recent market demand is for larger greenlip (Jim George, processor, personal communication). Thus, the substantial changes in the composition of the grades in the catch from FA 9 suggest that the population structure has changed over recent years, with an increased reliance on the harvest of smaller individuals following persistent, heavy fishing pressure on larger individuals. Fewer large greenlip in the catch reduces the mean weight of the individual abalone harvested, and requires more individuals to be extracted for similar total catch levels. These patterns are consistent with both the observations of the divers and the recent fishery-dependent data for the fishery, notably large reductions in CPUE and mean daily catch. Thus, the temporal patterns in the weight-grade data, along with the estimates of mean weight, provide additional evidence that confirms the most recent assessment of the fishery (Chick et al., 2009). The example from FA 9 demonstrates that analysis of changes in the composition of the grades in the catch over time can be a meaningful measure of change in the harvested stock. It also shows the value of measures derived from the composition of the grades in the catch over time as additional indices of stock status. These confirm that the weight-grade data should be used to aid assessment of the greenlip stocks in Region A. Notably, abalone fisheries have proven difficult to assess and manage, with well documented declines in production having been recorded (Prince, 2004). In contrast, this WZ fishery has demonstrated stable catches for >20 years, and recent assessments indicate the stocks are in a generally strong position (Chick et al., 2009). Individual transferable quotas exceed 22 t (whole weight) and licence values approach A$10 M. Management arrangements aim to retain this level of sustainability (Nobes et al., 2004), with heavy reliance on the outcomes of the regular fishery assessment reports. Although current scientific assessments of the abalone stocks in Region A are based on a balance of information and data from a range of sources, including catch and effort, biological and fishery-independent survey data (see Chick et al., 2009), the difficulties in assessing abalone fisheries suggests that higher levels of diversity in the available data will help to ensure more robust conclusions regarding stock status (Chen et al., 2003; Scheirer et al., 2004), and lower levels of uncertainty. As the management of fish stocks and fisheries depends fundamentally on reliable assessments, use of the weight-grade data as an additional information source should reduce the risk of uncertain, biased or erroneous advice (Hilborn, 1997; Rice, 1999; Chen et al., 2003). The current greenlip weight-grade data have two weaknesses. The first of these is its low resolution. This problem is consistent with that identified for lobster (Walker and Bentley, 2002) and prawn (Dixon et al., 2009; Roberts et al., 2009) weight-grade data, and arises because there are only three grades, with Grades 1 and 2 spanning a broad range of individual meat-weight values. This difficulty, along with changes to grading systems through time (Walker and Bentley, 2002), likely explains the apparent limited direct (or indirect) use of commercial weight-grade data in fishery assessments. For example, while the current data permit estimation of the maximum number of greenlip harvested, their low resolution prevents estimation of potentially more useful, alternative measures, such as an estimate of the actual number harvested. The potential value of these data to improving assessments of the WZ abalone fishery suggests that collection of more detailed weightgrade data is warranted in future years. Notably, improving the estimates of mean weight and number of individuals harvested

36

S. Mayfield / Fisheries Research 105 (2010) 28–37

could provide more informative performance measures for the fishery. This could be achieved by either increasing the resolution of the grading system, or obtaining the individual greenlip weights from the entire catch or a representative sub-sample of the graded catch. Implementation of the latter approaches would be costeffective through investment in automated weighing and grading systems. The second weakness is the inability of the weight-grade data to provide information on the length-structure of the commercial catch. This is because the analyses undertaken here provided no evidence of a functional, strong relationship between these two variables, with this problem extending to the smallest spatial and shortest temporal scales considered. While compounded by the coarse weight-grade categories, the primary reason for the poor relationships between these data sets is the low correlation between shell length and bled-meat weight. Although the strength of these relationships did improve when considered at finer spatial and temporal scales, a large proportion of the data were distributed within a narrow meat-weight range that spanned a broad range of shell lengths. The absence of strong, predictable relationships suggests that the use of the current weight-grade data as a measure of the length-structure of the commercial catch is inappropriate. The difficulty of replacing the current commercial catchsampling program with the weight-grade data re-emphasises the need for an appropriate catch-sampling program for the fishery (Andrew, 1996; Chick et al., 2009). This is because, consistent with abalone assessments undertaken elsewhere (Andrew and Chen, 1997; Tarbath et al., 2008), catch-sampling data are used to determine temporal changes in the length-structure of the commercial catch (Nobes et al., 2004; Chick et al., 2009). One potential strategy would be to use the weight-grade data in conjunction with data on the length-frequency distribution of the catch. Under this scenario (1) the highly representative, weight-grade data could be used to obtain information on the weight-structure of the catch across all fishing areas and licence holders with (2) future catchsampling programs being targeted to the most important fishing areas and map codes, for which the length-frequency data are more urgently required. This strategy could provide reliable data on the structure of the commercial catch, whilst reducing the onus on the commercial fishers to retain and measure shells from their catch. The high quality of the weight-grade data for greenlip, in conjunction with the high degree to which it represents the catch and fishery, suggest these data would improve fishery assessments of this species in the WZ. This is because analysis of changes in the composition of the grades in the catch, along with two derived measures, mean weight and number harvested, provide meaningful additional indices of stock status. Overcoming the low resolution of the weight-grade data would be significant, and could perhaps most easily be achieved by obtaining the individual greenlip weights from the graded catch using automated weighing and grading systems. While similar sampling of the blacklip catch in future years could aid assessments for this species, analysis of weight-grade data from other fisheries could also benefit their assessment. Acknowledgements Funds for this research were provided by Primary Industries and Resources South Australia, obtained through licence fees. SARDI Aquatic Sciences provided substantial in-kind support. Jim George and Hayden Myers (Western Abalone Processors Pty Ltd.), Gary White and Jonas Woolford (Abalone Down Under) and Damon Edmunds (Streaky Bay Marine Products) provided information on abalone processing. Kate Rodda, Brian Foureur, Peter Preece and Brian Davies undertook the diving and subsequent sampling to obtain the morphological data. Renée Saunders collated the weight-

grade and commercial length-frequency data. Paul Burch assisted with some of the statistical analyses. Ian Carlson created Fig. 1. Rowan Chick, Cameron Dixon, Thor Saunders, Mike Steer and an anonymous reviewer provided useful comments that substantially improved this manuscript. References Andrew, N.L., 1996. Review of the South Australian Abalone Research and Management Plan. Report to PISA, 35 pp. Andrew, N.L., Chen, Y., 1997. Optimal sampling for estimating the size structure and mean size of abalone caught in a New South Wales fishery. Fishery Bulletin 95, 403–413. Andrew, N.L., Worthington, D.G., Brett, P.A., 1997. Size-structure and growth of individuals suggest high exploration rates in the fishery for blacklip abalone, Haliotis rubra, in New South Wales, Australia. Molluscan Research 18, 275–287. Beddington, J.R., Agnew, D.J., Clark, C.W., 2007. Current problems in the management of marine fisheries. Science 316, 1713–1716. Bianchi, G., Gislason, H., Graham, K., Hill, L., Jin, X., Koranteng, K., ManickchandHeoleman, S., Paya, I., Sainsbury, K., Sanchez, F., Zwanenburg, K., 2000. Impact of fishing of size composition and diversity of demersal fish communities. Journal of Marine Science 57, 558–571. Campbell, A., 1992. Characteristics of the American lobster fishery of Grand Manan New Brunswick, Canada. Journal of Fisheries Management 12, 139–150. Casselman, J.M., Scott, K.A., Brown, D.M., Robinson, C.J., 1999. Changes in relative abundance, variability, and stability of fish assemblages of Eastern Lake Ontario and the Bay of Quinte – the value of long-term community sampling. Aquatic Ecosystem Health and Management 2, 25–269. Chick, R.C., Turich, N., Mayfield, S., Dent, J.J., 2009. Western Zone Abalone (Haliotis laevigata and H. rubra) Fishery (Region A). Fishery assessment report for PIRSA. SARDI Aquatic Sciences Publication No. F2007/000561-3. SARDI Research Report Series No. 376, 90 pp. Chen, Y., Chen, L., Stergiou, K.I., 2003. Impacts of data quantity of fisheries stock assessment. Aquatic Science 65, 92–98. Collins, S., 1986. An evaluation of AFZ squid logbook data and samples collected by AFZ of observers. Technical Report 14, Department of Sea Fisheries, 24 pp. DeMartini, E.E., Uchiyama, J.H., Williams, H.A., 2000. Sexual maturity. Sex ratio, and size composition of swordfish, Xiphias gladius, caught by the Hawaii-based pelagic longline fishery. Fishery Bulletin 98, 489–506. De Sousa, L.P., Brito, A., Abdula, S., Caputi, N., 2006. Research assessment for the management of the industrial shallow-water multi-species shrimp fishery in Sofala Bank in Mozambique. Fisheries Research 77, 207–219. Dixon, C.D., Hooper, G.E., Roberts, S.D., 2009. Spencer Gulf Prawn Penaeus (Melicertus) latisulcatus, Fishery 2007/2008. Fisheries Assessment Report to PIRSA Fisheries. South Australian Research and Development Institute (Aquatic Science), Adelaide, 109 pp., SARDI Publication Number F2007/000770-3. Fournier, D.A., Breen, P.A., 1983. Estimation of abalone mortality rates with growth analysis. American Fisheries Society 112, 403–411. Fowler, A.J., Steer, M.A., Jackson, W.B., Lloyd, M.T., 2008. Population characteristics of southern sea garfish (Hyporhamphus melanochir, Hemiramphidae) in South Australia. Marine and Freshwater Research 59, 429–443. Frank, K.T., Brickman, D., 2001. Contemporary management issues confronting fisheries science. Journal of Sea Research 45, 173–187. Gerritsen, H.D., McGrath, D., 2007. Precision estimates and suggested sample sizes for length-frequency data. Fisheries Bulletin 106, 116–120. Gorfine, H.K., 2001. Post-harvest weight loss has important implications for abalone quota management. Journal of Shellfish Research 20, 795–802. Haddon, M., 2000. Size-structure models of abalone populations with a cryptic component to the stock. Journal of Shellfish Research 19, 517–523. Hamasaki, K., Kitada, S., 2008. The enhancement of abalone stocks: lesson from Japanese case studies. Fish and Fisheries 9, 243–260. Heales, D.S., Brewer, D.T., Kuhnert, P.M., Jones, P.N., 2007. Detecting declines in catch rates of diverse trawl bycatch species, and implications for monitoring. Fisheries Research 84, 153–161. Heery, E.C., Berkson, J., 2009. Systematic errors in length frequency data and their effect on age-structured stock assessment models and management. Transactions of the American Fisheries Society 138, 218–232. Hilborn, R., Walters, C.J., 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York, USA. Hilborn, R., 1997. Uncertainty, risk and the precautionary principle. In: Pikitch, E.J., Huppert, D.D., Sissenwine, M.P., Duke, M.D. (Eds.), Global Trends: Fisheries Management. American Fisheries Society Symposium 20. Washington, USA, 100–106 pp. Hilborn, R., Annala, J., Holland, D.S., 2006. The cost of overfishing and management strategies for new fisheries on slow-growing fish: orange roughy (Hoplostethus atlanticus) in New Zealand. Canadian Journal of Fisheries and Aquatic Science 63, 2149–2153. Linnane, A., McGarvey, R., Feenstra, J., 2009. Southern Zone Rock Lobster (Jasus edwardsii) Fishery. Fisheries Assessment Report to PIRSA. South Australian Research and Development Institute (Aquatic Science), Adelaide, 107 pp., SARDI Publication Number F2007/000276-3. Maunder, M.N., Sibert, J.R., Fonteneau, A., Hampton, J., Kleiber, P., Harley, S.J., 2006. Interpreting catch per unit effort data to access the status of individual stocks and communities. ICES Journal of Marine Science 63, 1373–1385.

S. Mayfield / Fisheries Research 105 (2010) 28–37 McGarvey, R.M., Matthews, J.M., Prescott, J.H., 1997. Estimating lobster recruitment and exploitation rate from landings by weight and numbers and age-specific weights. Marine and Freshwater Research 48, 1001–1008. McGarvey, R., Matthews, J.M., 2001. Incorporating numbers harvested in dynamic estimation of yearly recruitment: onshore wind in interannual variation of South Australian rock lobster (Jasus edwardsii). ICES Journal of Marine Science 58, 1092–1099. McGarvey, R., Punt, A.E., Matthews, J.M., 2005. Assessing the information content of catch-in-numbers: a simulation comparison of stock assessment methods based on catch and effort totals. In: Kruse, G.H., Gallucci, V.F., Hay, D.E., Perry, R.I., Peterman, R.M., Shirley, T.C., Spencer, P.D., Wilson, B., Woodby, D. (Eds.), Fisheries Assessment and Management in Data-Limited Situations, Alaska Sea Grant College Program, University of Alaska, Fairbanks, 635–653 pp. McWhinnie, S.F., 2009. The tragedy of the commons in international fisheries: an empirical examination. Journal of Environmental Economics and Management 57, 321–333. Miller, T.J., Skalski, J.R., 2006. Integrating design- and model-based inference to estimate length and age composition in North Pacific longline catches. Canadian Journal of Fisheries and Aquatic Science 63, 1092–1114. Mora, C., Myers, R.A., Coll, M., Libralato, S., Pitcher, T.J., Sumaila, R.U., Zeller, D., Watson, R., Gaston, K.J., Worm, B., 2009. Management Effectiveness of the World’s Marine Fisheries 7, 1–11. Myers, R.A., Worm, B., 2003. Rapid worldwide depletion of predatory fish communities. Nature 423, 280–283. Nobes, M., Casement, D., Mayfield, S., 2004. Management Plan for the South Australian Abalone Fishery. South Australian Fisheries Management Series No. 42, 47 pp. Oh, C.W., Hartnoll, R.G., Nash, R.D.M., 1999. Population dynamics of the common shrimp, Crangon crangon (L.), in Port Erin Bay, Isle of Man, Irish Sea. Journal of Marine Science 56, 718–733. Peuser, R., Kline, L., 1997. Aging U.S. fisheries research vessels: implications for future collection of long-term fishery independent data. Fisheries 22, 18–19. Prince, J.D., 2004. The decline of global abalone (genus Haliotis) production in the late twentieth century: is there a future? In: Leber, K.M., Kitada, S., Svasand, T., Blankenship, H.L. (Eds.), Stock Enhancement and Sea Ranching, 2nd ed. Blackwell Publishing, Oxford, pp. 427–443. Prince, J.D., 2005. Combating the tyranny of scale for Haliotids: micro-management for microstocks. Bulletin of Marine Science 76, 557–577. Prince, J.D., Peeters, H., Gorfine, H.K., Day, R.W., 2008. The novel use of harvest policies and rapid visual assessment to manage spatially complex abalone resources (genus Haliotis). Fisheries Research 94, 330–338. Prince, J.D., Shepherd, S.A., 1992. Australian abalone fisheries and their management. In: Shepherd, S.A., Tegner, M.J., Guzmán del Próo, S.A. (Eds.), Abalone of

37

the world: biology, fisheries and culture, Fishing News Books, Oxford, United Kingdom, 407–426 pp. Punt, A.E., Kennedy, R.B., 1997. Population modelling of Tasmanian rock lobster, Jasus edwardsii, resources. Marine Freshwater Research 48, 967–980. Punt, A.E., 2003. The performance of a size-structured stock assessment method in the face of spatial heterogeneity in growth. Fisheries Research 65, 391–409. Rice, J., 1999. Stock assessment of target species. In: Nolan, C.P. (Ed.), Proceedings of the International Conference on Integrated Fisheries Monitoring, 51–63 pp. Roberts, S.D., Dixon, C.D., Hooper, G.E., 2009. Gulf St Vincent Prawn, Penaeus (Melicertus) latisulcatus, Fishery 2007/2008. Fisheries Assessment Report to PIRSA Fisheries. South Australian Research and Development Institute (Aquatic Science), Adelaide, 133 pp., SARDI Publication Number F2007/000782-3. Rotherham, D., Underwood, A.J., Chapman, M.G., Gray, C.A., 2007. A strategy for developing scientific sampling tools for fishery-independent surveys of estuarine fish in New South Wales, Australia. ICES Journal of Marine Science 64, 1512–1516. Saunders, T.M., Mayfield, S., 2008. Predicting biological variation using a simple morphometric marker in the sedentary marine invertebrate Haliotis rubra. Marine Ecology Progress Series 366, 75–89. Saunders, T.M., Mayfield, S., Hogg, A., 2008. A simple, cost-effective, morphometric marker for characterising abalone populations at multiple spatial scales. Marine and Freshwater Research 59, 32–40. Scheirer, K., Chen, Y., Wilson, C., 2004. A comparative study of American lobster fishery sea and port sampling programs in Maine: 1998–2000. Fisheries Research 68, 343–350. Shears, N.T., Grace, R.V., Usmar, N.R., Kerr, V., Babcock, R.C., 2006. Long-term trends in lobster populations in a partially protected vs. no-take Marine Park. Biological Conservation 132, 222–231. Tarbath, D., Mundy, C., Haddon, M., 2008. Tasmanian abalone fishery 2007. TAFI Fishery Assessment Report, ISSN 1441-8487, 82 pp. Walker, N., Bentley, N., 2002. A compilation of commercial weight-grade data from the Chatham Islands rock lobster fishery (CRA6). New Zealand Fisheries Assessment Report 2002/45, 14 pp. Worm, B., Barbier, E.B., Beaumont, N., Duffy, E., Folke, C., Halpern, B.S., Jackson, J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., Sala, E., Selkoe, K.A., Stachowicz, J.J., Watson, R., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790. Worm, B., Hiblorn, R., Baum, J.K., Branch, T.A., Collie, J.S., Costello, C., Fogarty, M.J., Fulton, E.A., Hutchings, J.A., Jennings, S., Jensen, O.P., Lotze, H.K., Mace, P.M., McClanahan, T.R., Minto, C., Palumbi, S.R., Parma, A.M., Ricard, D., Rosenberg, A.A., Watson, R., Zeller, D., 2009. Rebuilding global fisheries. Science 325, 578–585. Zar, J.H., 1984. Biostatistical Analysis, 2nd ed. Prentice-Hall, New Jersey.