Spatial patterns of recreational exploitation in ... - Wiley Online Library

Fisheries Management and Ecology Fisheries Management and Ecology, 2014, 21, 383–397

Spatial patterns of recreational exploitation in eastern Australian ROFAs: implications for zonal management F. A. OCHWADA-DOYLE NSW Department of Primary Industries, Sydney Institute of Marine Science, Mosman & School of Biological, Earth and Environmental Science, University of New South Wales, Sydney, NSW, Australia

J. MCLEOD NSW Department of Primary Industries, Port Stephens Fisheries Institute, Nelson Bay, NSW, Australia

G. BARRETT & G. CLARKE NSW Department of Primary Industries, Fisheries, Nowra, NSW, Australia

C. A. GRAY WildFish Research, Grays Point, NSW, Australia & School of Biological, Earth and Environmental Science, University of New South Wales, Sydney, NSW, Australia

Abstract Effective management of recreational fisheries requires information on fine-scale spatial patterns of recreational exploitation. Such information is particularly important for specially designated regions such as recreationalonly fishing areas (ROFAs). Using data acquired through progressive counts and interview-based surveys, this study quantitatively compared recreational effort, harvest-per-unit-effort (HPUE) and species composition among zonal habitats in three representative estuarine ROFAs in eastern Australia. The zones compared were as follows: (1) entrance channels; (2) lake areas; (3) artificial reefs; (4) tributary creeks; (5) rivers; and (6) canals. In most cases, effort was concentrated in the lake zones, which had the greatest access to fisheries resources. The lake and channel zones were associated with some of the highest HPUEs for key taxa [Acanthopagrus spp. (hybrid complex of Acanthopagrus butcheri (Munro) 9 Acanthopagrus australis (Owen)), Platycephalus fuscus (Cuvier), Sillago ciliata (Cuvier) and Girella tricuspidata (Quoy & Gaimard)] and a greater number of highly sought-after species. Drawing on specific examples from these findings, this paper concludes by illustrating how spatial information on exploitation gained from this type of research can be used to meet the fundamental goals of recreational fisheries management at fine spatial scales. ANOSIM, creel surveys, fisheries management, generalized linear models, recreational fishing, recreational fishing havens.

KEYWORDS:

Introduction Fishing is a major factor modifying global aquatic ecosystems (Crowder et al. 2008), and recent studies suggest that a considerable proportion of its impacts as well

as its socio-economic benefits are attributable to recreational fishing (Post et al. 2002; Coleman et al. 2004b; Cooke & Cowx 2006). The recreational fishing sector comprises approximately 11.5% of the world’s population, and an estimated 12% of global fishery harvests are

Correspondence: Faith A. Ochwada-Doyle, NSW Department of Primary Industries, Sydney Institute of Marine Science, 19 Chowder Bay Rd, Mosman, NSW 2088, Australia (e-mail: [email protected])

© 2014 John Wiley & Sons Ltd

doi: 10.1111/fme.12087

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credited to this sector (Cooke & Cowx 2004; Crowder et al. 2008). Recreational fisheries therefore represent lucrative global industries that must be carefully managed to meet three inextricable goals: (1) provide quality fishing opportunities to enable the longevity of recreational fisheries; (2) enable the conservation of aquatic populations and ecosystems that sustain recreational fisheries; and (3) ensure equitable allocation of resources among recreational and commercial fishing sectors (Mardle et al. 2002; Pereira & Hansen 2003; Sutinen & Johnston 2003). One strategy that has been attempted in some developed countries to meet these goals is the establishment of recreational-only fishing areas (also known as ROFAs or Recreational Fishing Havens) (Schroeder & Love 2002; Denny & Babcock 2004; Tobin 2010). ROFAs are geographic areas within multisector fisheries where commercial fishing is excluded but recreational fishing is still permitted (Tobin & Sutton 2011). In Australia, ROFAs have been implemented in all states and territories, and their main objectives have been to improve the quality of recreational fishing and to reduce conflict between commercial and recreational fishers (Tobin 2010; Tobin & Sutton 2011). It has also been suggested that ROFAs have the potential to conserve aquatic populations by maintaining or improving the biomass of some fish stocks (Bucher 2006; Tobin 2010). An integral part of assessing the performance of ROFAs and managing the species targeted within them is evaluating the fine-scale spatial patterns of recreational exploitation in ROFAs (Steffe & Chapman 2003; Bucher 2006). Such spatial information can be especially valuable for fine-scale zonal management given the generally patchy distribution of species and the often false assumption that fishing effort and harvest are spatially homogenous within estuaries (Parnell et al. 2010). This study examined spatial patterns of recreational exploitation for four key targeted species/taxa [Acanthopagrus spp. (hybrid complex of Acanthopagrus butcheri (Munro) 9 Acanthopagrus australis (Owen)), Platycephalus fuscus (Cuvier), Sillago ciliata (Cuvier) and Girella tricuspidata (Quoy & Gaimard)] in three estuarine ROFAs on the east coast of Australia. Specifically, this study compared three factors among the estuaries’ spatial zones: (1) recreational boat- and/or shore-based harvest-per-unit-effort (HPUE) for the key taxa; (2) total recreational fishing effort; and (3) the composition of species in recreational catches. To conclude, this paper draws on examples to illustrate how spatial information on recreational exploitation can be used to meet the three goals of recreational fisheries management within ROFAs and other estuaries.

Materials and methods Study sites and temporal sampling frame

Three eastern Australian estuaries (declared as ROFAs in 2002) were examined in this study: Lake Macquarie, St. Georges Basin and Tuross Lake (Fig. 1). The physical characteristics and zonal habitats of each of these estuaries are summarised in Table 1. Stratification into the various zones in each estuary mainly reflected spatial differences in fish habitats, fishing practices and perceived recreational fishing quality (Steffe & Chapman 2003, p. 4; Ghosn et al. 2010, p. 12). However, the northern lake area (NL) and southern lake area (SL) zones in Lake Macquarie and Tuross Lake and the lower river (lRv) and upper river (uRv) zones in Tuross Lake were characterised by similar habitats and relatively similar fishing practices and were separated in the survey design for logistical ease given their large sizes (Pollock et al. 1994, p. 33; Steffe & Chapman 2003). In examining species composition, these areas were therefore combined to form single general zones which are referred to, hereafter, as the lake (L) and river (Rv) zones (note that this coarse spatial stratification did not result in different findings on species composition from those observed when the finer stratification was applied). In examining effort and HPUE, ignoring the finer spatial stratification resulted in inflated variances and these parameters were therefore estimated independently for the NL, SL, lRv and uRv zones in Lake Macquarie and/or Tuross Lake. Among the estuaries, two fisheries were examined in this study: a boat-based recreational sector and a shorebased recreational sector (Steffe & Chapman 2003). Boat-based fishers accessed all zones in Lake Macquarie, St. Georges Basin and Tuross Lake, respectively, using thirty-five, ten and four diffusely distributed access points (mainly boat ramps). Points from which shorebased fishers in Lake Macquarie and St. Georges Basin could access fisheries resources were similarly diffuse and numerous, spanning each of this estuary’s zones apart from the artificial reef (AR) zone. Tuross Lake’s shoreline mainly consists of dense vegetation and large private properties that preclude shoreline access to fisheries resources (Steffe et al. 2005a). The shore-based fishery of this estuary was consequently not assessed. The temporal sampling frame of this study spanned 3 months, beginning 1 March 2011. This period was stratified into day-types [weekday (WD) and weekend days (WE)]. All public holidays were classified as WE days. For St. Georges Basin and Lake Macquarie, three WD and three WE days within each month were randomly selected (without replacement) as survey days on which effort data would be collected, with selection of survey © 2014 John Wiley & Sons Ltd

PATTERNS OF EXPLOITATION IN ROFAS

Figure 1. Relative location of Lake Macquarie, St. Georges Basin and Tuross Lake in NSW, Australia. Also shown is the location and extent of the zones examined within each estuary. These zones are: Lake Macquarie: channel (Ch), northern lake (NL), southern lake (SL) and artificial reefs (AR); St. Georges Basin: channel (Ch), lake area (L), canals (Cl), creek (Cr) and artificial reefs (AR); and Tuross Lake: channel (Ch), northern lake (NL), southern lake (SL), lower river (lRv) and upper river (uRv). Double sided arrows indicate survey boarders.

days within each day-type stratum and in each estuary occurring separately. For these two estuaries, three WD and three WE days within each month were independently selected, using similar methods, as survey days on which HPUE data would be collected. In Tuross Lake, effort and HPUE data were collected on the same three, randomly selected WD and WE days within each month. The primary sampling unit used was days, and sampling of effort and HPUE data would therefore occur on n = 9 WD days and n = 9 WE days during the survey. For St. Georges Basin and Tuross Lake, effort and HPUE data were collected between sunrise and sunset. © 2014 John Wiley & Sons Ltd

For Lake Macquarie sampling of effort and HPUE data was restricted to between 9:00 am and sunset because this estuary was much larger and therefore more expensive to sample. Omission of fishing trips completed before 9:00 am was justified because data from a previous study in Lake Macquarie showed that, respectively, only 3.6 and 4.2% of boat- and shore-based fishing trips were completed between sunrise and 9:00 am (Steffe et al. 2005b), suggesting that most day-time recreational fishing trips in this estuary that start within this period would be covered by a 9:00 am to sunset sampling regime (Steffe & Chapman 2003).

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Table 1. The physical characteristics (NSW Office of Environment & Heritage 2011) and zonal habitats of each of Lake Macquarie, St. Georges Basin and Tuross Lake

Estuary

Surface area (km2)

Lake Macquarie

114.10

604.40

5.70

St. Georges Basin

40.90

315.80

5.30

Tuross

15.50

1813.80

1.20

Catchment area (km2)

Mean depth (m)

Zones (1) a shallow tidal channel (Ch); (2) a northern lake area (NL); (iii) a southern lake area (SL) and (3) a series of artificial reefs [constructed from pH balance micro-silica concrete within fibreglass moulds that are treated to create a rough textured surface that successfully promotes settling by marine organisms (Lowry et al. 2010)] within deep waters (AR) (1) a shallow tidal channel (Ch); (2) a broader and deeper lake area (L); (3) a series of shallow canals (Cl); (4) a shallow and narrow creek (Cr) that flows on from the lake; and (5) a series of artificial reefs within deep waters (AR) (1) a tidal channel (Ch); (2) a broad northern lake area (NL); (3) a southern lake area of lesser breadth (SL); (4) a narrow lower river area (lRv); and (5) a narrow upper river area (uRv)

Collection of effort data

Roving progressive counts of all individual boats and individual shore-based persons engaged in any fishing activity were used to collect data on boat- and shorebased recreational fishing effort in St. Georges Basin and Lake Macquarie. To make the progressive counts, a single circuit traversing all zones was navigated by boat. The starting times of the progressive counts for each day were scheduled by randomly selecting one of a set of discrete possible starting times (Hoenig et al. 1993). The starting location and direction of travel within the circuit were also randomly selected on each day. For Tuross Lake, effort for the boat-based fishery was estimated from counts of all boat-parties involved in recreational fishing that returned to the four access points on the survey days. Collection of these count data was prioritised over interviewing fishing parties during busy periods. Relative to progressive counts, this method provided a more cost-effective, yet accurate and validated, way of quantifying effort in Tuross Lake given its smaller size, but precluded estimation of zonal effort (Steffe et al. 2005a). Collection of HPUE data

In St. Georges Basin and Tuross Lake, a trained surveyor was stationed at each of the boat-based access points on each day selected for collection of HPUE data. This access-point survey design enabled surveyors to interview all returning, cooperative, boat-based fishing parties using standardised, machine readable interview forms. For each party, the forms allowed the following information to be recorded: (1) duration of fishing and non-fishing activity; (2) zones fished and duration of fishing in each zone; and (3) numbers and sizes of species retained as harvest (obtained by the surveyor

inspecting harvest) and zones in which they were caught. Although surveyors could be stationed at each major boat-based access point (these were generally larger and able to accommodate high boating traffic) in Lake Macquarie to collect HPUE data, minor boat-based access points (27 in total) were numerous and it was not costeffective to station surveyors at each of them. Twelve minor access points were therefore randomly selected as points to station surveyors, with the selection process being renewed for each survey day (Pollock et al. 1994). The same methods and forms as those described above were used in Lake Macquarie. Areas from which shore-based fishers could access fisheries resources in Lake Macquarie and St. Georges Basin were diffuse, and this precluded the use of accesspoint survey methods to estimate shore-based HPUE. A roving survey design that relied on incomplete trip interviews was therefore used (Pollock et al. 1994; Steffe & Chapman 2003). On each day selected for collecting HPUE data, trained surveyors travelled by car, boat or foot to traverse all shoreline access areas and interviewed all cooperative shore-based fishers they intercepted throughout the survey day using similar forms as those described above. Daily selection of the starting location and direction of travel was random.

Statistical analyses

The calculations used to derive and compare effort and HPUE estimates were performed in the open-source software R (Ihaka & Gentleman 1996). The equations for expansion are described in detail by Pollock et al. (1994), and Steffe and Chapman (2003) and provided in the supplementary material (Table S1). The number of boats or shore-based fishers counted in each zone on each day selected for sampling effort in Lake Macquarie © 2014 John Wiley & Sons Ltd

PATTERNS OF EXPLOITATION IN ROFAS

and St. Georges Basin was multiplied by the average length of a fishing day to estimate the boat- and shorebased fishing effort for each sampled day (note that the distribution of the number of hours fished per day in each fishery of each estuary was not significantly different from the normal distribution (one-sample Kolmogorov–Smirnov test: D = 0.20–0.30, P = 0.33–0.73), thereby justifying the use of the average length of a day to scale up). For each fishery in each estuary, the daily effort estimates were expanded to calculate zonal mean effort for each day-type stratum followed by total zonal effort across WD and WE days (and their variances and standard errors). For Tuross Lake, the number of fishing boat-parties observed on each survey day was totalled across access points and multiplied by the average length of a day to estimate daily boat-based effort. The estimates were expanded to calculate mean effort for each day-type stratum followed by total effort across daytypes (and their variances and standard errors). The final units for boat- and shore-based effort, respectively, were boat hours and fisher hours. Boat- and/or shore-based HPUEs of key species from each estuary were estimated from the interview data. For the boat-based fisheries, HPUE was calculated for Acanthopagrus spp., P. fuscus and S. ciliata because: (1) they were among the top-five harvested taxa in the boat-based catch from all three estuaries; (2) they are highly sought after by the recreational sector of New South Wales (Henry & Lyle 2003; Steffe & Chapman 2003; Ghosn et al. 2010); and (3) they are among the main taxa captured in the recreational line-only fishery of south-eastern Australia (Gray et al. 2011). Based on the same criterion, shore-based HPUE was calculated for Acanthopagrus spp., P. fuscus and G. tricuspidata. Because the boat-based interview data were based on complete trip information from an access-point design, the estimator for daily HPUE was the ratio of means, which is essentially the harvest averaged across boat-parties in a day divided by the average effort across boatparties on that day (Pollock et al. 1994). This ratio is the correct estimator of daily HPUE when trip duration does not affect a boat-party’s probability of being selected for an interview (Pollock et al. 1994, p. 179). The shore-based interview data were based on incomplete trip information, and the probability of selecting an angler for an interview was proportional to the length of the angler’s fishing trip (Pollock et al. 1994). The shorebased estimator for daily HPUE was therefore the mean of ratios, which is calculated by dividing each angler’s harvest by their effort and then dividing the sum of these quotients for a day by the number of anglers interviewed on that day (Pollock et al. 1994, p. 179). For each of the boat- or shore-based species in each estuary, esti© 2014 John Wiley & Sons Ltd

mates of mean daily HPUE, as well as their variances and standard errors, were calculated separately for each day-type stratum in each zone. They were further expanded to estimate the seasonal HPUE of each species across day-types, weighting the contribution of each day-type stratum by the relative number of days belonging to each day-type stratum within the season. Calculations of shore-based HPUE were performed on truncated data that excluded very short trips (those that lasted AR SL>Ch SL>AR Ch>AR (b) Boat-based fishery ARCh L>Cr Shore-based fishery L>Cr Cl>Cr Ch>Cr

P(>|t|)

10
3 10
4 10
5 10
4 10
3

1.19 2.60 2.00 1.56 1.31

9 9 9 9 9

1.90 0.04 4.22 2.84 4.22

9 10
3 9 10
3 9 10
4 9 10
3

1.86 9 10
7 1.28 9 10
7 1.10 9 10
8

spp. were harvested at a significantly greater rate on WE days and within the L and AR zones (Fig. 2a; Tables 5a and 7). Zonal variation in the HPUE of this taxon was not detected in Tuross Lake, but HPUEs during WE days © 2014 John Wiley & Sons Ltd

were significantly higher (Table 5a; Fig. 2a). In Lake Macquarie, the boat-based HPUE of P. fuscus only showed significant variation among zones with the SL zone having the greatest rates (Fig. 2b; Tables 5b and 7). The mean HPUE of P. fuscus was significantly higher on WE days in St. Georges Basin, and the highest rates among zones were observed in the Ch, L and AR zones (Fig. 2b; Tables 5b and 7). In Tuross Lake, these HPUEs were equally high among all zones apart from the uRv (Table 5b; Fig. 2b). The boat-based HPUE of S. ciliata in Lake Macquarie was highest on the WE days and within the NL, SL and Ch zones (Fig. 2c; Tables 5c and 7). Temporal and zonal differences in the boat-based HPUE of S. ciliata were not significant in St. Georges Basin and Tuross Lake (Table 5c; Fig. 2c). Although the WD HPUE for S. ciliata in the lRv zone appeared quite large, its standard error was also very large and likely lead to the statistical similarity among zones. The shore-based HPUE of Acanthopagrus spp. was similar among zones and day-types in each estuary (Table 6a; Fig. 3a). This was also the case for G. tricuspidata in St. Georges Basin (Table 6c; Fig. 3c). The HPUE of P. fuscus in Lake Macquarie and St. Georges Basin were similar among day-types but highest in the NL and SL zones and in the L zone, respectively (Fig. 3b; Tables 6b and 7). In Lake Macquarie, HPUE of G. tricuspidata was higher within the Ch zone (Fig. 3c; Tables 6c and 7). A summary of the overall spatial patterns of total HPUE in each fishery within

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Table 5. Analyses of deviance for generalised linear models used to determine the influence of day-type and zone on the mean daily boat-based harvest-per-unit-effort (HPUE) for (a) Acanthopagrus spp., (b) Platycephelus fuscus and (c) Sillago ciliata in Lake Macquarie, St. Georges Basin and Tuross Lake. The models for each species in each estuary were fitted assuming a quasi-Poisson distribution. The probabilities of significant terms (a = 0.05) in the models are highlighted in bold d.f. (a) Lake Macquarie Null Day-type Zone St. Georges Basin Null Day-type Zone Tuross Lake Null Day-type Zone (b) Lake Macquarie Null Day-type Zone St. Georges Basin Null Day-type Zone Tuross Lake Null Day-type Zone (c) Lake Macquarie Null Day-type Zone St. Georges Basin Null Day-type Zone Tuross Lake Null Day-type Zone

Deviance

Residual d.f.

Residual deviance

F

1 3

0.52 0.79

71 70 67

18.59 18.07 17.28

1.66 0.83

1 4

0.87 9.13

89 88 84

26.35 25.48 16.36

4.36 11.43

0.04 2.16 3 10
7

1 4

1.88 1.42

89 88 84

19.62 17.75 16.33

7.53 1.42

7.00 3 10
3 0.23

1 3

0.06 1.87

71 70 67

8.08 8.01 6.14

0.65 6.32

0.42 7.72 3 10
4

1 4

1.10 6.89

89 88 84

20.12 19.02 12.13

5.57 8.69

0.02 6.43 3 10
6

1 4

0.52 4.16

89 88 84

30.08 29.56 25.40

1.48 2.94

0.23 0.02

1 3

0.37 1.11

71 70 67

4.64 4.26 3.16

6.91 6.77

0.01 4.66 3 10
4

1 4

0.07 2.22

89 88 84

12.39 12.32 10.10

0.25 2.00

0.62 0.10

1 4

0.96 3.17

89 88 84

17.68 16.73 13.55

1.99 1.65

0.16 0.17

each estuary is given in the total harvest (number quarie, St. Georges Basin vey period was 139 777 5139 (1092).

Figure 4. Across all fisheries, of fish) (SE) for Lake Macand Tuross Lake over the sur(23 597), 30 375 (5828) and

Relative species composition in retained harvest among zones

There were significant differences among zones in terms of the species present in the boat-based harvest of Lake

P(>F)

0.20 0.48

Macquarie (R = 0.53, P = 1.00 9 10
3), St. Georges Basin (R = 0.31, P = 1.00 9 10
3) and Tuross Lake (R = 0.34, P = 1.00 9 10
3). Pairwise comparisons of the zones revealed significant differences in the species retained between all pairs of zones in Lake Macquarie (R = 0.40–0.68, P = 1.00 9 10
3 in all cases); all pairs of zones in St. Georges Basin (R = 0.24–0.96, P = 1.00 9 10
3 to 4.00 9 10
3 in all significant cases) apart from Ch and Cr and Ch and AR; and all pairs of zones in Tuross Lake (R = 0.41–0.46, P = 1.00 9 10
3 in all significant cases) apart from L © 2014 John Wiley & Sons Ltd

PATTERNS OF EXPLOITATION IN ROFAS

Figure 2. Mean (SE) daily boat-based harvest-per-unit-efforts (HPUEs) for (a) Acanthopagrus spp., (b) Platycephelus fuscus and (c) Sillago ciliata in different zones within Lake Macquarie, St. Georges Basin and Tuross Lake. The HPUEs are partitioned into day-types [weekend (white bars) and weekday (grey bars)]. Table 6. Analyses of deviance for generalised linear models used to determine the influence of day-type and zone on the mean daily recreational shore-based harvest-per-unit-effort (HPUE) for (a) Acanthopagrus spp., (b) Platycephelus fuscus and (c) Girella tricuspidata in Lake Macquarie and St. Georges Basin. The models for each species in each estuary were fitted assuming a quasi-Poisson distribution. The probabilities of significant terms (a = 0.05) in the models are highlighted in bold d.f. (a) Lake Macquarie Null Day-type Zone St. Georges Basin Null Day-type Zone (b) Lake Macquarie Null Day-type Zone St. Georges Basin Null Day-type Zone (c) Lake Macquarie Null Day-type Zone St. Georges Basin Null Day-type Zone

deviance

Residual d.f.

Residual deviance

F

P(>F)

1 2

0.05 0.51

53 52 50

13.71 13.66 13.14

1.66 0.83

0.20 0.48

1 2

0.08 1.73

53 52 50

17.11 17.03 15.31

0.19 2.18

0.66 0.12

1 2

0.01 0.94

53 52 50

6.15 6.14 5.20

0.04 3.29

0.84 0.04

1 2

0.12 1.99

53 52 50

5.19 5.07 3.08

1.62 12.96

0.21 2.93 3 10
5

1 2

0.39 11.83

53 52 50

22.88 22.49 10.67

1.16 17.58

0.29 1.65 3 10
4

1 2

0.19 1.11

53 52 50

6.78 6.59 5.48

0.82 2.38

0.37 0.10

© 2014 John Wiley & Sons Ltd

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Figure 3. Mean (SE) daily shore-based harvest-per-unit-efforts (HPUEs) for (a) Acanthopagrus spp., (b) Platycephelus fuscus and (c) Girella tricuspidata within different zones in Lake Macquarie and St. Georges Basin. The HPUEs are partitioned into day-types [weekend (white bars) and weekday (grey bars)]

and Rv. There was a significant difference between the L and Ch zones in terms of the species present in the shore-based harvest of Lake Macquarie (R = 0.69, P = 1.00 9 10
3) and among zones in St. Georges Basin (R = 0.18, P = 4.00 9 10
3). Pairwise tests revealed that only the L and Cl zones were significantly different in St. Georges Basin (R = 0.13–0.33, P = 3.00 9 10
3 to 0.03 in all significant cases). The species that contributed to the average similarity within each zone in each fishery of each estuary are given Tables 8 and 9. Discussion The immediate management benefits of studies that examine spatial patterns of recreational exploitation include the following: (1) defining optimal areas for implementing tools for enhancing specific parts of a recreational fishery and enabling evaluation of the effectiveness of such tools; (2) facilitating the development of spatially specific management and conservation policies for heavily exploited recreational species; and (3)

increasing the capacity of managers to negotiate spatially precise, and therefore optimal, compromises on resource allocation. The following section draws on examples from the current findings to illustrate how the information gained from this type of study can be applied to achieve these goals and thus contribute to the management of recreational fisheries. Defining optimal areas for implementing tools for fishery enhancement and assessing their effectiveness

Effort was generally highest in the lake zone, especially for the boat-based fisheries. This zone provided the greatest accessibility to fisheries resources. Numerous global studies have suggested that ease of access to resources, alongside other factors such as perceived probability of success based on past experience, is a major factor influencing site choice among recreational anglers and, subsequently, the intensity of fishing effort (e.g. Post et al. 2002; Denny & Babcock 2004; Kent et al. 2010). With the exception of the artificial reefs in Lake Macquarie and St. Georges Basin, the relative © 2014 John Wiley & Sons Ltd

PATTERNS OF EXPLOITATION IN ROFAS

Table 7. Significantly different pairs of zones within Lake Macquarie, St. Georges Basin and Tuross Lake in terms of the (a) boat-based and (b) shore-based harvest-per-unit-effort (HPUE) for Acanthopagrus spp., Platycephelus fuscus, Sillago ciliata or Girella tricuspidata. Significant differences between pairs of zones were determined by testing the null hypotheses that the partial regression coefficients (bi) from generalised linear models, which expressed the difference between the two zones compared, were not significantly different from zero Different pairs (a = 0.05) (a) Lake Macquarie P. fuscus NLCh SL>AR S. ciliata NL>AR SL>AR Ch>AR St. Georges Basin Acanthopagrus spp. Ch>L L>Cr L>Cl ChCl L>Cr ClCh G. tricuspidata Ch>NL Ch>SL St. Georges Basin P. fuscus Ch>L L|t|)

0.047 0.011 40 9 10
4 0.007 0.017 0.006

0.043 0.027 0.027 0.027 0.027 0.011 0.006 0.007 0.003 0.002 0.011 0.011

0.014 0.024 0.021

0.041 0.029 0.014

0.003 0.003

amount of boat-based effort in each zone also appeared to be influenced by the relative area of each zone with the larger zones generally having the highest levels of effort. Accessibility appeared to play the greatest role in influencing shore-based effort because levels of shorebased effort did not correlate with the size of the zones. © 2014 John Wiley & Sons Ltd

The channel and lake zones as well as the canal zone of St. Georges Basin had the highest levels of shore-based access, leading to higher effort. The patterns of effort uncovered here suggest that future measures aimed at enhancing boat-based fishing opportunities, for instance, would be most beneficial if implemented within the lake zones of the estuaries examined. These measures might include restoration of seagrass beds within lake zones, structural improvement of boat ramps within lake zones to enable them to handle higher boating traffic or stock enhancement of species that use the lake zones. It is surprising that boat-based effort within the artificial reefs was consistently low compared with other areas considering the substantial area occupied by these habitats in Lake Macquarie and St. Georges Basin and considering their accessibility. It is also surprising given the popular perception among recreational fishers that these structures have the potential to improve fishing quality and increase the occurrence of certain target species (Lowry et al. 2010). This finding, which is contrary to reports of increased fishing effort within artificial reefs in other parts of the world (e.g. Bohnsack & Sutherland 1985; Grossman et al. 1997), suggests that the reefs in the estuaries studied did not necessarily attract greater recreational fishing activity. This raises pertinent questions on the realised effects of implementing artificial reefs as opposed to their perceived/potential benefits. These questions must be assessed prior to the establishment of any future artificial reefs using a combination of carefully designed before-after-impacted-control studies and attitudinal surveys (e.g. Sutton & Tobin 2009; Tobin 2010). It should be noted, however, that the current findings may simply reflect a lack of public awareness of the existence or precise location of the artificial reefs and expose a need to promote them better because HPUE was not consistently low within the artificial reefs. Developing spatially specific management and conservation policies for heavily exploited species

The four taxa examined here, Acanthopagrus spp., P. fuscus, S. ciliata and G. tricuspidata, are among the main taxa/species captured in the recreational line-only fishery and the commercial gillnet and beach seine fisheries of south-east Australian estuaries and have each attained an exploitation status of fully fished (Rowling et al. 2010; Gray et al. 2011). Careful management of their stocks is therefore needed to ensure their long-term conservation within estuaries. This is especially true for ROFAs because recreational fishing pressure within such systems has the potential to significantly reduce the abundance and size structure of targeted species (Westera

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Figure 4. Total (across all species harvested) seasonal boat- and shore-based harvest-per-unit-effort (HPUE) (SE) within different zones in Lake Macquarie, St. Georges Basin and Tuross Lake.

Table 8. SIMPER analysis revealing the species that typified the retained boat-based harvest in (a) (1) the lake, (2) the channel, and (3) the artificial reefs of Lake Macquarie; (b) (1) the lake, (2) the channel, (3) the canals, (4) the creek and (5) the artificial reefs of St. Georges Basin; and (c) (1) the lake, (2) the channel, and (3) the river of Tuross Lake Group

Average similarity

(a) Lake Macquarie Lake

57.50

Channel

51.39

Artificial Reefs (b) St. Georges Basin Lake

42.12

Channel Canals Creek Artificial Reefs

37.31 77.78 16.67 53.50

(c) Tuross Lake Lake

69.38

Channel

51.39

River

42.12

54.86

Species

Contribution (%)

Cumulative contribution (%)

Aldrichetta forsteri Platycephalus fuscus Portunus pelagicus Acanthopagrus spp. Pomatomus saltatrix Acanthopagrus spp. P. fuscus Sillago ciliata Pomatomus saltatrix Acanthopagrus spp.

16.50 13.33 13.33 11.49 9.63 18.67 18.67 11.95 10.03 48.32

16.50 29.82 43.15 54.64 64.27 18.67 37.34 49.29 59.32 48.32

Acanthopagrus spp. P. fuscus Pagrus auratus P. fuscus Acanthopagrus spp. P. fuscus Acanthopagrus spp. P. fuscus

20.61 20.61 15.23 59.00 100.00 100.00 42.26 42.26

20.61 41.21 56.44 59.00 100.00 100.00 42.26 84.53

P. fuscus A. forsteri P. fuscus Acanthopagrus spp. A. forsteri

62.52 27.60 45.90 26.14 46.49

62.52 90.12 45.90 72.05 46.49

et al. 2003; Denny & Babcock 2004). Relating information on patterns of recreational extraction for such exploited species to their known habitat/zonal associations can assist in the development of spatially explicit management policies. The generally higher HPUE of P. fuscus in the channel and lake zones and of G. tricuspidata in the channel zone, for example, were probably

linked to spatial associations that these species have with the marine dominated waters in the lower reaches of estuaries when P. fuscus and G. tricuspidata are in spawning condition (Gray et al. 1990; Rowling et al. 2010). These areas correspond with the entrance channels and lake areas closest to the entrance of most estuaries. Temporary seasonal closures of recreational fishing © 2014 John Wiley & Sons Ltd

PATTERNS OF EXPLOITATION IN ROFAS

Table 9. SIMPER analysis revealing the species that typified the retained shore-based harvest in (a) (1) the lake and (2) the channel of Lake Macquarie; and (b) (1) the lake, (2) the channel and (3) the canals of St. Georges Basin Group

Average similarity

Species

Contribution (%)

(a) Lake Macquarie Lake Channel

46.02 42.12

Aldrichetta Forsteri Girella tricuspidata Acanthopagrus spp.

57.53 35.20 32.43

57.53 35.20 67.63

Acanthopagrus spp. Platycephalus Fuscus Acanthopagrus spp. Acanthopagrus spp.

39.65 32.42 61.29 48.25

39.65 72.07 61.29 48.25

(b) St. Georges Basin Lake 46.08 Channel Canals

32.13 21.49

within these zones in Lake Macquarie and St. Georges Basin during the spawning period of P. fuscus and G. tricuspidata may enable conservation of their spawning stock [note that although slightly longer, the spawning period of P. fuscus (November to March) is similar to that of G. tricuspidata (October–January) within southern NSW (Gray 2008; Gray et al. 2012)]. Such measures have previously been recommended with some evidence that they can mitigate loss of spawning stock (Shipp 2003; Nemeth 2005), but there is also evidence that they can lead to temporal displacement of effort resulting in intense exploitation in other seasons (Coleman et al. 2004a). Their effectiveness must therefore be assessed prior to large-scale application. Increasing the capacity to negotiate spatially precise compromises on resource allocation

In the current spatial comparisons of species composition in retained harvest, four of the five SIMPER analyses revealed that a greater number of highly sought-after species (e.g. Acanthopagrus spp., P. fuscus, Pomatomus saltatrix, Portunus pelagicus and Pagrus auratus) typified the retained catch of the lake zone compared with other zones. Based on the same criteria, retained catches from the channel zone appeared to be characterised by the second highest number of coveted species. The highest total HPUE was also consistently observed in the lake and channel zones. If future research shows that the dual trends of higher recreational HPUE in lakes and channels and a greater occurrence of sought-after species in catches from these zones can be generalised across several geographic and temporal scales, fisheries managers may find it beneficial to only designate such zones as ROFAs in other estuaries. This is because leaving other zones open to commercial fishing could minimise opposition to ROFAs from the commercial sector and reduce commercial licence buyout costs (see Tobin 2010). However, the effectiveness of such measures © 2014 John Wiley & Sons Ltd

Cumulative contribution (%)

would have to be assessed before they could be implemented broadly. Such measures may also have to be accompanied by other management initiatives (e.g. temporal closures during spawning season) aimed at ensuring that concentrated recreational effort within only the channel and lake zones does not result in over-exploitation of the spawning biomass. The patterns of recreational exploitation uncovered in the estuaries studied only apply for the time period examined (autumn 2011). These patterns may vary between seasons and years as shown for estimates of effort, HPUE and harvest in a previous survey conducted in Lake Macquarie (Steffe & Chapman 2003) and as evidenced in a recent qualitative interannual comparison of autumn survey results from Lake Macquarie (OchwadaDoyle et al. 2014). This highlights the need to conduct future surveys more regularly and during alternating seasons or at wider temporal intervals so that a series of seasonally heterogeneous snapshots of recreational activity within ROFAs can be accumulated. Conclusion Effective spatial management of fisheries within specially zoned areas not only requires an understanding of biological patterns of habitat utilisation among species, it also necessitates fine-scale information on spatial patterns of fishery HPUE and effort (Crowder et al. 2006; Douvere 2008; Parnell et al. 2010). This study has illustrated the utility of survey methods in gaining spatial information on recreational exploitation and demonstrates how such information can be used to meet the fundamental goals of recreational fisheries management. Acknowledgments We thank the NSW Recreational Fishing Trust and the NSW Department of Primary Industries for funding this project. Thanks also to the numerous field-based casual

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staff and contractors who assisted in collecting the survey data. Our gratitude is extended to A. Steffe, J. Murphy, J. Craig, J. Hughes, P. Butcher, M. Lowry, D. Ghosn, D. Collins, P. Somerfield and K. Pollock who provided invaluable advice and support on the design, validation, analyses and reporting of these data. References Anderson M.J., Gorley R.N. & Clarke K.R. (2008) PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. Plymouth: PRIMER-E Ltd., 214 pp. Bohnsack J.A. & Sutherland D.L. (1985) Artificial reef research: a review with recommendations for future priorities. Bulletin of Marine Science 37, 11–39. Bucher D.J. (2006) Spatial and temporal patterns of recreational angling effort in a warm temperate Australian estuary. Geographical Research 44, 87–94. Clarke K.R. & Gorley R.N. (2006) PRIMER v6: User Manual/ Turtorial. Plymouth: PRIMER-E Ltd., 190 pp. Coleman F.C., Baker P.B. & Koenig C.C. (2004a) A review of Gulf of Mexico marine protected areas. Fisheries 29, 10–21. Coleman F.C., Figueira W.F., Ueland J.S. & Crowder L.B. (2004b) The impact of United States recreational fisheries on marine fish populations. Science 305, 1958–1960. Cooke S.J. & Cowx I.G. (2004) The role of recreational fishing in global fish crises. BioScience 54, 857–859. Cooke S.J. & Cowx I.G. (2006) Contrasting recreational and commercial fishing: searching for common issues to promote unified conservation of fisheries resources and aquatic environments. Biological Conservation 128, 93–108. Crowder L.B., Osherenko G., Young O.R., Airamac S., Norse E.A., Baron N. et al. (2006) Resolving mismatches in U.S. ocean governance. Science 313, 617–618. Crowder L.B., Hazen E.L., Avissar N., Bjorkland R., Latanich C. & Ogburn M.B. (2008) The impacts of fisheries on marine ecosystems and the transition to ecosystem-based management. Annual Review of Ecology, Evolution, and Systematics 39, 259–278. Denny C.M. & Babcock R.C. (2004) Do partial marine reserves protect reef fish assemblages? Biological Conservation 116, 119–129. Douvere F. (2008) The importance of marine spatial planning in advancing ecosystem-based sea use management. Marine Policy 32, 762–771. Elith J. & Leathwick J.R. (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40, 677–697. Fox J. (2008) Generalized Linear Models. Applied Regression Analysis and Generalized Linear Models. Thousand Oaks, CA: Sage, 379–424 pp. Ghosn D.L., Steffe A.S. & Murphy J.J. (2010) An Assessment of the Effort and Catch of Shore-Based and Boat-Based

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Supporting Information

Additional Supporting Information may be found in the online version of this article: Table S1. The expansion techniques used to calculate recreational fishing effort and harvest per unit of effort (HPUE) from survey data collected from Lake Macquarie, St. Georges Basin and Tuross Lake between March 1 to May 31, 2011.

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