The use of a Roving Creel Survey to monitor exploited coastal fish species in the Goukamma Marine Protected Area, South Africa

by

Carika Sylvia van Zyl

A thesis submitted in fulfillment of the requirements for the degree of

Masters in Technoligae, Nature Conservation

Nelson Mandela Metropolitan University

2011

i

I, Carika Sylvia van Zyl (s208027504) hereby declare that the work in this document is my own.

ii

Abstract

A fishery-dependant monitoring method of the recreational shore-based fishery was undertaken in the Goukamma Marine Protected Area (MPA) on the south coast of South Africa for a period of 17 months. The method used was a roving creel survey (RCS), with dates, times and starting locations chosen by stratified random sampling. The MPA was divided into two sections, Buffalo Bay and Groenvlei, and all anglers encountered were interviewed. Catch and effort data were collected and catch per unit effort (CPUE) was calculated from this. The spatial distribution of anglers was also mapped.

A generalized linear model (GLM) was fitted to the effort data to determine the effects of month and day type on the variability of effort in each section. Fitted values showed that effort was significantly higher on weekends than on week days, in both sections. A total average of 3662 anglers fishing 21 428 hours annually is estimated within the reserve with a mean trip length of 5.85 hours.

Angler numbers were higher per unit coastline length in Buffalo Bay than Groenvlei, but fishing effort (angler hours) was higher in Groenvlei. Density distributions showed that anglers were clumped in easily accessible areas and that they favored rocky areas and mixed shores over sandy shores.

Catch documented between October 2008 and December 2009 included a total of 361 fish, of 27 species from 12 families. Sparidae had the highest contribution (12 species). A Shannon-Weiner diversity index showed that diversity was higher in Buffalo Bay (0.81) than Groenvlei (0.57). Catch composition of retained fish (336 individuals) showed that the six numerically most important species were blacktail (Diplodus sargus capensis) (66% of catch), followed by galjoen (Dichistius capensis) at 11 %, Cape stumpnose (Rhabdosargus holubi), belman (Umbrina robinsonii) and strepie at 3%, and elf (Pomatomus saltatrix) at 2 %.

Catch composition of an earlier study in Goukamma (Pradervand and Hiseman 2006) was compared with the present study, as well as data from the De Hoop MPA, which

iii is closed to fishing. A multi-dimensional scaling plot of catch composition showed tight clustering of the De Hoop samples, and high variability among the Goukamma samples. A bray-curtis similarity index and dendrogram of similarity between study sites and study periods showed that there was an 83% similarity among De Hoop samples and a 75% similarity among Goukamma samples (ignoring the two outliers). The two sites are different with respect to species composition, but this is expected because they are different areas. Differences between time periods in Goukamma (i.e. the previous study versus the present study) were not significant. The most significant result from the catch composition analyses is the high variability among the Goukamma samples. This can be explained by the variable fishing methods used by anglers in Goukamma, compared with the standardized fishing methods used by researchers in De Hoop, and the fact that fish are more abundant and populations are more stable in De Hoop – giving higher sample sizes which reduce the variability in the statistics.

Species-specific CPUE was calculated for the six numerically most important species. In both sections, CPUE was highest for blacktail, with an average of 0.133 fish per hour for Groenvlei, and 0.060 fish per hour for Buffalo Bay, over the 12 months. The second highest CPUE values per section were 0.030 for galjoen in Groenvlei and 0.039 for strepie in Buffalo Bay. Remaining CPUE values ranged from 0.014 (belman in Groenvlei) to the lowest value of 0.001 (strepie in Groenvlei). Total estimated CPUE for these six species in the MPA using the estimated effort and catch results amounted to 0.018 fish per hour.

An annual estimated 3897 fish were landed in the reserve during 2009. Most fish (n=2481, 64%) were caught in the Groenvlei section. Numbers of blacktail were the highest of all species, within both sections (2353 fish). Strepie was the next most common (561 fish), but was caught almost entirely within the Buffalo Bay section (97% of individuals), followed by galjoen (548 fish) caught mostly within the Groenvlei section (92% of individuals).

Size comparisons of the six species between the Goukamma and De Hoop MPAs showed that ranges in size are similar, but there are substantial differences in mean sizes between the two MPAs. Sample sizes of all species from the Goukamma MPA were too small to draw conclusions about stock status, except for blacktail. iv

The Goukamma MPA is a popular fishing destination and angler effort is high. It can be considered a node of exploitation for surf zone fish, for which it provides no protection. Even though the MPA allows shore angling, sustainable fishing practices should be incorporated in management plans if the MPA is expected to protect and conserve its stocks. Of noteworthy concern is the occurrence of illegal night fishing (the public may not enter the reserve between sunrise and sunset) which leads to underestimates of catch and effort (night surveys were not conducted because of safety concerns).

It is recommended that more communication should take place between the angling community and the reserve management. Sign boards giving information on species which are under pressure, and why they are under pressure, with a short explanation on their life cycles, is advised.

The roving creel survey method was suitable for the study area and delivered statistically rigorous results. I thus recommend that it is continued in the future by management. I make some recommendations for reducing costs of future surveys, as well as for altering the survey design if funds are very limited.

v Table of Contents Page

Chapter 1 1 Introduction 1 1.1 Problem Statement 1 1.2 Background 1 1.3 Study Area 7 1.3.1 Location 7 1.3.2 Vegetation and topography 8 1.3.3 Intertidal habitat and subtidal reefs 8 1.3.4 Access to coastline 11

Chapter 2 13 Review of monitoring methods for shore based fishing 13 2.1 Background 13 2.2 History of monitoring of shore-based fishing in South Africa 14 2.3 Review of monitoring methods for shore-based fishing 20 2.3.1 Methods for monitoring shore-based fishing 20 Fishery-dependent and -independent data 20 Off-site and on-site survey methods 21 Sampling frames 22 Off-site surveys 23 (i) Mail surveys 23 (ii) Telephone surveys 24 (iii) Door-to-door surveys 25 (iv) Logbooks, diaries and catch cards 26 On-site surveys 27 (i) Roving creel surveys (RCS) 27 (ii) Access point surveys 28 (iii) Aerial surveys 28 The present study 29

vi Chapter 3 30 Methods: the roving creel survey 30 3.1 Background 30 3.2 Week day versus weekend day sampling ratio 32 3.3 Questionnaire 35 3.4 Effort, Catch and CPUE 36 3.4.1 Background 36 3.4.2 Length frequencies 42 3.4.3 Spatial analysis 44 3.4.4 Comparison of catch composition among three data sets and diversity index 45

Chapter 4 46 Results and Discussion: Angler effort 46 4.1 Temporal distribution of anglers 46 4.1.1 Estimated angler averages - Buffalo Bay 48 4.1.2 Estimated angler averages - Groenvlei 48 4.2 Estimated angler hours 50 4.3 Spatial distribution of anglers 52 4.3.1 Angler density per habitat type 52 4.3.2 Angler density per 100 m section 53 4.4 Discussion 54

Chapter 5 57 Results and Discussion: 57 Catch composition, catch per unit effort and total Catch 57 5.1 Catch composition 57 5.1.1 Comparison of catch composition between three data sets 59 5.2 Size distribution of retained fish 64 5.3 Catch per unit effort 66 5.3.1 Size composition of individual species 68 (i) Blacktail 68 (ii) Galjoen 70 (iii) Strepie 72

vii (iv) Cape stumpnose 73 (v) Belman 74 (vi) Elf 76 5.4 Catch Estimates 78 5.5 Discussion 80

Chapter 6 85 Conclusions and recommendations 85

Glossary 92

References 94

Appendices (Electronic – please see attached CD)

viii List of Figures Figure 1 Inshore bioregions of South Africa (Lombard et al. 2004) and the Goukamma study site. Figure 2 The Goukamma Marine Protected Area showing the two sampling sections. Figure 3 The Goukamma MPA boundary and reef systems. Figure 4 Intertidal habitats found in the Goukamma Marine Protected Area (based on Clark and Lombard 2007). Figure 5 Western access to the Goukamma Marine Protected Area at Platbank (red arrow). Figure 6 Eastern access to the Goukamma Marine Protected Area (red arrows). Figure 7 Comparisons of different week day (WD): weekend day (WE) sampling ratios, and different number of surveys per month, with respect to the total fishing effort sampled. Figure 8 The average number of anglers encountered per month during 2009 in the Groenvlei (GV) and Buffalo Bay (BB) sections of the Goukamma MPA. Bars indicate one standard error and Y axis average number of anglers. Figure 9 GLM predicted average number of angler counts through the year, and by day type, at Buffalo Bay. WE = weekend days and WD = week days. Figure 10 GLM predicted average number of angler counts through the year, and by day type, at Groenvlei. WE = weekend days and WD = week days. Figure 11 Predicted numbers of hours fished, per month, in the two sections of the Goukamma MPA. Bars indicate one standard error. Figure 12 Number of anglers per km of intertidal habitat in the Goukamma MPA during the study period. Figure 13 Angler density per 100m interval along the entire length of the Goukamma MPA during the study period (orange lines indicate access points). Figure 14 A multi-dimensional scaling (MDS) plot of catch composition at Goukamma and De Hoop MPAs for several years. The three data sets all used different monitoring techniques. Goukamma WWF (G2009) refers to the present study.

ix Figure 15 Cluster analysis of the same data sets used in Figure 14. G2009 (red) refers to the present study; green indicates the De Hoop data set. Figure 16 Size distribution of blacktail caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size. Figure 17 Size distribution of blacktail caught during surveys at De Hoop MPA from 1984 - 2010. Figure 18 Size distribution of galjoen caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size. Figure 19 Size distribution of galjoen caught during surveys at De Hoop MPA from 1984 – 2010. Figure 20 Size distribution of strepie caught in the Goukamma MPA from August 2008 – December 2009. Figure 21 Size distribution of Cape stumpnose caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size. Figure 22 Size distribution of belman caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size. Figure 23 Size distribution of belman caught during surveys at De Hoop MPA from 1984 – 2010. Figure 24 Size distribution of elf caught in the Goukamma MPA from August 2008 – December 2009. Figure 25 Size distribution of elf caught during surveys at De Hoop MPA from 1984 – 2010.

x

List of Tables Table 1 Length of the seven intertidal habitats found in the Goukamma Marine Protected Area. Table 2 Distribution of the number of anglers encountered on week days (WD) and weekend days (WE) during the five month pilot study (Step 1). Step 2 shows how these data were used to apportion the 12 surveys per month that the study could afford for the 2009 survey period. Table 3 Estimated monthly effort sampled using the 12 surveys a month, apportioned as three week days and nine weekend days. Table 4 Number of roving creel surveys conducted in the Goukamma MPA for the period August 2008 – December 2009. Table 5 Length frequency equations used for the numerically important species. Table 6 GLM results of the effect of angler effort on month and day type in the Groenvlei section of the MPA. Table 7 Estimated monthly effort expressed as hours fished per section within the Goukamma MPA. Table 8 Results of a Chi square analysis of anglers encountered within the seven different intertidal habitats. Table 9 Number (and percentage) of retained fish, per species, in the two sections of the Goukamma MPA (October 2008 – December 2009). Table 10 Results of the mean Bray Curtis dissimilarity of catch composition between the De Hoop data set and the Goukamma WWF 2009 data set. Table 11 Sizes of all documented fish caught in the Goukamma MPA from October 2008 – December 2009. Table 12 Catch per unit effort of six numerically important species in the two sections of the Goukamma MPA. Table 13 Estimated numbers of fish caught of the six numerically important

species for the year 2009.

xi Acknowledgements

I would like to thank the various donors for their financial support without which this project would not have been successful. The financial assistance from the NMMU, WWF, Fairfields Travel and Pumpcor is hereby acknowledged.

To my supervisor, Dr AT Lombard thank you for the time that you dedicated towards this project and the skills that you have passed on to me; especially towards the end where many hours of reading were required. I feel honoured to have had you as my supervisor. On the same note, I would like to thank Assoc Prof. C.G. Attwood who acted like a supervisor, guiding me to set up the data base and with the analysing and interpretation of the data. He also allowed me to use his fishery independent data at De Hoop which stretched over 25 years for comparative analysis. To Roland Scholtz my friend and assistant, who braved thousands of kilometres with me, in all weather conditions over very rough terrain, in search of anglers. The high quality of your work and knowledge of the fishery contributed immensely to our success, thank you.

Gratitude towards CapeNature is expressed for allowing us to conduct the monitoring program in the reserve. Especially Keith Spencer, the manager of Goukamma for supporting this project in every possible way. This included arranging accommodation at Groenvlei, sponsoring the “Fishing for the Future” workshop, mentorship, creating a paid student position when funding was insufficient to employ a second person and your friendship (and for letting Maya stay). Karen Bekker from CapeNature for doing media releases on the research. The Breezers Fishing shop in Knysna who donated prizes which helped in the success of the workshop.

To Jade Maggs from the Oceanographic Research Institute (ORI) for making available the catch card data used for the comparative analysis.

A special word of thanks to all the people and friends who joined us on the walks; especially to Alex Munro and Christine McCagh who selflessly gave of their time to do pick ups, drop offs and walks. This could not have been done without you.

Sincere gratitude is expressed towards the angling community of Goukamma for their support and participation in the Goukamma Marine Research Project. I know it was xii not always easy giving of your precious ‘alone time’ to answer all our questions. I am grateful for all the anecdotal stories, help and friendliness.

This research would not have been possible without the support of my family and friends. I would like to thank my mother Trudie van Zyl for her ongoing spiritual, emotional and financial support, during my six years of studies, I love you so much. My beautiful family, Judy, Callie, Herman, Lettie, Phillip and Nelita thank you for all the constant love while I was so far away from home in order to follow my dreams. To my best friend Tracey Lotter, thank you for the constant support and belief in me. The ‘bende’; Eva, Wentzel, Jan, Odie, Tracey, Zoe, Zane, Annalie, Gillie, Sabrina and Jill.

And finally to the Spirit of Creation, for leading me down the road not often walked and opening my eyes to the functioning and inter connectedness of life…I give thanks

Together change is possible … ‘Vir die toekoms van die planeet’

xiii Chapter 1

Introduction

1.1 Problem Statement

In 2000, the Minister of Environmental Affairs and Tourism declared South Africa’s linefishery in a state of emergency because of the crisis in this fishery (Government Gazette, 29 December 2000 No. 21949, Notice 4727 of 2000). Over-exploitation of linefish stocks together with the lack of management and monitoring of the fisheries have been highlighted as contributing factors to the emergency. One control measure to combat overfishing has been to institute Marine Protected Areas (MPAs) to protect, and provide recovery opportunity for, the exploited fish stocks. The effectiveness of MPAs, however, is brought into question when shore-based recreational fishing is allowed within MPA boundaries. It is thus important to monitor MPA effectiveness by determining total catch, and catch per unit effort, of linefish within MPA boundaries. As no such monitoring is currently undertaken within the Goukamma MPA, it is not possible to draw any conclusions regarding the sustainability of fishing within the reserve.

1.2 Background “Ecological extinction caused by overfishing precedes all other pervasive human disturbance to coastal ecosystems, including pollution, degradation of water quality, and anthropogenic climate change.” -Jackson et al. 2001

Nationally and internationally fish stocks are dwindling owing to overwhelming fishing pressure (Hutchings and Lamberth 2002, Bause et al. 2005, Berkes et al. 2006, Cooke and Cowx 2004; 2006, Beckley et al. 2008, Granek et al. 2008). The South African government declared the linefisheries in a legal state of emergency in 2000, owing to severe declines in catch rates (Griffiths 2000). This afforded government the power to better manage the resource (Attwood and Farquhar 1999, Griffiths 2000).

The linefish sector can be roughly broken up into commercial, subsistence and recreational components. Recreational linefishing is defined as the manual capture of fish with a hook and line using a fishing rod or hand line, and excludes long liners.

1 Commercial fishing is done only from boats while recreational and subsistence fishing is undertaken from either the shore or boats (Mann 2000, Harris et al. 2002, Götz et al. 2008). There are an estimated 750 000 recreational shore-based anglers in South Africa making this a very popular activity. The combined recreational effort is calculated at 3.2 million angler days/year with a collective catch of 3 000 tons (Bennett and Attwood 1991, Brouwer et al. 1997, Pradervand and Hiseman 2006, Beckley et al. 2008).

Globally, the ecological impacts of fishing have been widely studied and impacts can be at a population, community or ecosystem level. Changes in species diversity, size frequency distribution and life history traits have been documented. Populations decrease in size as older, larger fish are removed (these are often the most fecund and predatory in nature). Stocks are also under pressure of being genetically overfished and most have adapted to local environmental conditions with populations expressing unique gene pools (Sobel and Dahlgren 2004, Conover et al. 2005). Loss of the strongest genes can weaken populations and make them vulnerable to environmental and anthropogenic catastrophes (Attwood et al. 1997a). At a community level, the removal of fish from the upper trophic levels alters the trophic structure, causing a ‘top down effect’ and a shift in the ecological equilibrium (Attwood and Farquhar 1999, Attwood 2003, Cooke and Cowx 2004, Sobel and Dahlgren 2004, Götz 2005, Campbell and Pardede 2006, Kerwath et al. 2007, Götz et al. 2009a; 2009b). Species are thus sequentially depleted by anglers ‘shifting effort’ from one to the next. This causes a break in the flow of energy in marine food webs and places the marine environment at great ecological risk (Berkes et al. 2006).

Control measures that have been instituted to combat exploitation of species and to regulate and guide fisheries utilisation include the proclamation of MPAs. MPAs are often zoned for different activities. Some zones may allow certain extractive activities (e.g. shore-based fishing), but other zones do not allow any form of extraction (commonly referred to as ‘no-take’ zones). Closed seasons can also give protection to stock during vulnerable times of their life-cycles (e.g. spawning). These spatial and temporal closures can be seen as a means to rebuild depleted fish stocks, protect against collapse, and improve fishery yield recoveries (Attwood and Bennett 1995a, Buxton and Smale 1989, Attwood et al. 1997a, Francis et al. 2002, Gell and Roberts 2003, Groom et al. 2006). MPAs can also serve as a benchmark to compare exploited versus unexploited areas and to help guard against a ‘shifting baseline syndrome’, 2 where each generation of fishing stakeholders accepts a lower standard of resource abundance as normal (Ainsworth et al. 2008). MPAs also drive and initiate an ecosystem-based approach to marine management, which holistically benefits the oceans (Attwood et al. 1997b, Browman and Stergiou 2005). The current MPA Expansion Strategy of the South African government is to have 10 % of South Africa’s coast fully protected against any fishing pressure or anthropogenic impacts by the year 2020.

Presently about 17 % of the coast has protection status, but only 4.9 % is protected against all forms of exploitation (i.e. no-take). The percentage is even smaller if protection of the continental shelf is considered (Attwood, in press). The Marine Living Resources Act (MLRA 1998) limits angling and caps effort through closed seasons, closed areas and daily bag and species limits, but there is no limit to the number of anglers allowed to fish per day. Access is very widespread along the coastline and anglers are often sparsely distributed. This makes the control and documentation of catch difficult for authorities (Attwood and Bennett 1995a, MLRA 1998).

The study site, Goukamma Nature Reserve and Marine Protected Area (CapeNature), was proclaimed 20 years ago. It allows recreational angling from the shore but not from boats. Spear fishing and bait removal are not permitted. No scientific monitoring has been undertaken to determine catch and effort by recreational anglers in the Goukamma MPA. The only monitoring data available are those from compliance management and Oceanographic Research Institute (ORI) catch cards. This method monitors catch on an infrequent basis and can not be used to calculate species-specific CPUE (Catch per unit effort) values which are necessary to calculate abundance of fish species (Attwood pers. comm. 2009). Consequently, the data have limited ability to allow for statistically-sound conclusions to be drawn regarding the spatial and temporal distribution of catch and fishing effort. According to Attwood (In press) surf zone fish assemblages need dedicated monitoring techniques because they are seldom found outside the cover of broken surf.

Pradervand and Hiseman (2006) conducted an initial assessment of recreational angling in the Goukamma MPA. ORI catch cards and compliance management data over a ten- year period (1993-2002) were analysed. The assessment gave an indication of catch but was unable to calculate spatial, total and annual fishing effort or catch estimates. 3 Results did indicate that over the ten-year period, 30 % of catches were under the legal minimum size limit (as defined by the MLRA). Owing to the fact that the MPA plays an important nursery function for species such as juvenile white steenbras (Lithognathus lithognathus) there is cause for concern if juvenile fish are caught and retained, thereby impacting the local reproductive capacity of the stock (Bennett 1993).

Severe sampling biases were highlighted during the course of data collection, because sampling was done opportunistically by law enforcement staff of CapeNature. Comparisons with formal fisheries monitoring methods gathered by neutral staff could thus not be made, as there was a lack of comprehensive patrol data. The study concluded that the fishery was perceived to be sustainable, but strongly recommended that a suitable scientific sampling protocol be implemented (Pradervand and Hiseman 2006).

Owing to these reasons, this MPA was chosen as a study site. The only perceived impact on the coastal fish stock is that of recreational anglers and all other impacts such as pollution, boat based fishing and commercial activity are considered absent.

As the MPA is small (15 km of coastline, extending 1nm offshore) protection is given only to small populations of deeper reef dwelling fish such as roman (Chrysoblephus laticeps) (Götz 2005). Species targeted by rock and surf anglers rarely occur outside of the high-energy, wave-exposed sandy beaches and shores of mixed rock and sand and therefore need dedicated monitoring techniques (Attwood and Swart 2010).

Habitats that are fished by shore-based anglers such as the estuary and shallow reefs, form critical parts of multiple life stages for mature and immature fish i.e. spawning, feeding, nursery and migration (Cooke and Cowx 2004). Many of the collapsed species, i.e. galjoen (Dichistius capensis), kob (Argyrosomus spp.), white steenbras, musselcracker (Sparodon durbanensis), poenskop (Cymatoceps nasutus), rockods (Epinephelus spp.) and roman occur within the MPA. They have complex life cycles, are mostly top predators, slow growing, change sex and have small home ranges making them particularly vulnerable to over-exploitation (Buxton and Smale 1989, Buxton and Clarke 1991, Bennett 1993, Griffiths, 1997b, Attwood and Cowley 2005, Kerwath et al. 2007, Götz et al. 2009a). Studying the catch rates of fish communities within the MPA before any possible future closures to shore-based recreational angling will allow one to 4 determine whether the closures have promoted recovery of fish communities (Bennett and Attwood 1991). This monitoring will provide the baseline data, with which future results can be compared.

Rock and surf anglers target a multitude of these species with a high catch collectively, but low catch per species. If effort is shifted from one species to the next it might seem that the collective catch rate remains stable, while individual species catch rates are actually declining. Stock assessments of each targeted species are required to monitor rates of exploitation on a time series. This is achieved by calculating individual CPUE (fish per angler hour) with the use of creel surveys. CPUE can be used as an indicator to measure abundance and population recovery of species-specific stocks as symptoms of exploitation include a decrease in mean size and density of targeted species (Bennett and Attwood 1991).

The lack of an adequate assessment and review of the status of species-specific stocks is a conservation concern especially within the Goukamma MPA, because there has been no monitoring done on the exploitation rate within this protected area. Attwood et al. (1997a) suggest that monitoring of fish stocks should be integral in MPA management and can serve as a ‘pristine’ control against which exploited areas can be measured. Cooke and Cowx (2006) believe that without improved management and monitoring, recreational fisheries may not be sustainable in the long term. Monitoring of recreational shore-based catches has been largely overlooked as the fishery is difficult to manage and many researchers have highlighted the need for a uniform monitoring technique in South Africa to gather quantitative data on recreational fishing effort (Attwood and Bennett 1995a, Brouwer et al. 1997, Attwood 2003, Pradervand and Hiseman 2006, Prior and Beckley 2006, Attwood pers. comm.). Most previous monitoring efforts in South Africa and Goukamma were snapshots and were not continuous, and there was no uniformity in monitoring techniques. Results were of local relevance only and sites could not be compared with one another statistically (Attwood 2003, Götz 2005).

Based on the recommendations of Pollock et al. (1994), this study uses the roving creel survey (RCS) method to gather data on recreational shore-based fishing in the Goukamma MPA. Two datasets were collected: (i) data on trends in CPUE, and (ii) data on the spatial distribution of fishing effort. Goukamma was chosen as the study site 5 because it is in the same marine bioregion as the no-take MPA of De Hoop, in which controlled fishery-independent monitoring is currently being undertaken. By using standardised methods to obtain catch and effort data, a statistically comparable analysis can be done between the no-take MPA and Goukamma. This will allow us to make recommendations regarding the effectiveness on small MPAs that allow shore angling, and will provide a baseline to monitor the Goukamma fishery in the future (provided the RCSs are continued) (Buxton and Smale 1989, Bennett and Attwood 1993, Attwood 2003, Pradervand and Hiseman 2006, Götz et al. 2007a;b, Attwood 2009).

The five research questions addressed in this study are: 1. How much effort is expended by recreational shore-based anglers in the Goukamma MPA? 2. How is angler effort distributed in space and time? 3. Which species are anglers harvesting from the MPA and what is the total catch of each? 4. What is the species-specific CPUE of the MPA? 5. How does the Goukamma CPUE compare with no-take MPAs in the same marine bioregion?

This study reports on data collected from 2008 (Aug-Dec) and 2009 (Jan-Dec). Currently, additional data are being gathered by another survey team for 2010 (Jan- Dec). Once surveys are completed in December 2010, the additional data will be analysed by Assoc. Prof. C.G. Attwood and Dr A.T. Lombard, and manuscript will be prepared by van Zyl, Attwood and Lombard, for publication in a peer-reviewed scientific journal (for example, the African Journal of Marine Science, Aquatic Conservation: Marine and Freshwater Ecosystems, Biological Conservation, Ecological Applications, Marine and Freshwater Research, or Ocean and Coastal Management.

6 1.3 Study Area

1.3.1 Location

The Goukamma MPA (-34° 03’ to -34° 06’ S; 22° 50’ to 23° E) is situated 20 km west of Knysna in the Southern Cape, South Africa, and forms part of the Agulhas marine Bioregion (Figures 1 and 2). It was proclaimed in 1960 as a nature reserve and in 1990 as a MPA (Robinson and de Graaff 1994, Clark and Lombard 2007).

Figure 1. Inshore bioregions of South Africa (Lombard et al. 2004) and the Goukamma study site.

Figure 2. The Goukamma Marine Protected Area showing the two sampling sections.

7 1.3.2 Vegetation and topography

Sandy soils of coastal origin, over sedimentary rocks of the Algoa, Bredasdorp and Bokkeveld Group, support Southern Cape Dune Fynbos and Southern Coastal Forest within the Goukamma Nature Reserve. Floral diversity is low, but the reserve is the most prominent example of this vegetation unit. High undulating coastal sand dunes cover a great part of the reserve and these stabilised old calcareous dunes are known to be the highest vegetated sand dunes in South Africa (Mucina and Rutherford 2006).

The coastal lake Groenvlei forms part of the reserve covering a 250 ha surface area and is 3.7 km long and 0.9 km wide. The Goukamma River estuary (20 ha) is temporarily open for parts of the year (Robinson and de Graaff 1994).

1.3.3 Intertidal habitat and subtidal reefs

The dune system forms part of the Wilderness Dune Cordons which originated during the Pleistocene inter-glacial periods (Illenberger 1996). The area has undergone several phases of dune building and coastal erosion over the last million years. Sandy and mixed shores dominate the coastline (Figure 3), and sand movement is very dynamic owing to the turbulent wave action and strong currents found in the MPA. The coastline is ever changing. The inter-tidal and sub-tidal reefs (10.4 km2) are found predominantly in the eastern end of the MPA in the Buffalo Bay section, and extend into the sea. These were formed by aelonite sandstone that developed from coalesced dune deposits which became eroded with recent sea-level rise (Illenberger 1996). Sub-tidal reefs extend two miles offshore to a depth of 37 m and a rocky area runs along the entire southern boundary (Götz 2005).

8

Figure 3. The Goukamma MPA boundary and subtidal reef systems.

As described by Clark and Lombard (2007), seven distinct intertidal habitats (the area between the Mean High Water Spring and Mean Low Water Spring) represent four primary level habitats i.e. rocky shores, mixed shores, sandy shores and river outlets (Figure 4, Table 1).

9

ROCKY SHORES Rock_Exposed Rock_Very exposed

MIXED SHORES Sand above rock_Exposed Broken rock and sand_Exposed Scattered rock and sand_Exposed SANDY SHORES Sand (intermediate) N RIVERS Estuary temporarily closed W E

S

Figure 4. Intertidal habitats found in the Goukamma Marine Protected Area (based on Clark and Lombard 2007).

Table 1. Length of the seven intertidal habitats found in the Goukamma Marine Protected Area.

Intertidal Habitat Habitat % of length m total length Sand above rock exposed 123.5 0.77 Broken rock and sand exposed 416.6 2.61 Rock very exposed 541.4 3.39 Estuary temporarily closed 827.8 5.19 Rock exposed 985.6 6.17 Scattered rock and sand exposed 5259.2 32.95 Sand (intermediate) 7808.2 48.92 Total 15962.3 100

10 The 15 km coastline is dominated by intermediate sandy shores (48.9 %), containing particles < 4 mm, followed by scattered rock and sand (32.9 %). Rainfall is bimodal, but occurs all year with a mean of 757 mm per annum. Mean daily temperatures are 25.3°C and 8.0°C for February and July respectively, and frost is uncommon owing to the strong marine influence (Mucina and Rutherford 2006). Marine protection stretches one nautical mile into the ocean. Prohibited activities within the MPA include boating, boat-based fishing, the removal of bait organisms, spear and night fishing.

1.3.4 Access to coastline

N

W E

S

Figure 5. Western access to the Goukamma Marine Protected Area at Platbank (red arrow).

There is only one access point in the West (Figure 5). It is from the Platbank parking area, which has road access (the yellow line) to the National Road (which runs along the northern boundary of Groenvlei). Anglers park their vehicles at Platbank and walk along the shoreline of the Groenvlei section of the MPA. There is no vehicle access onto the beach, or into the terrestrial component of the Goukamma Nature Reserve.

11

N N W W E E

S S

Figure 6. Eastern access to the Goukamma Marine Protected Area (red arrows).

There are many access points in the East (Figure 6). These are from the roads leading to, and within, the coastal town of Buffalo Bay. There are many parking areas (green) and access points (red arrows) onto the shoreline of the Buffalo Bay section in the MPA. Again, there is no vehicle access onto the beach, or into the terrestrial component of the Goukamma Nature Reserve.

12 Chapter 2 Review of monitoring methods for shore based fishing

2.1 Background

The oceans cover 70 % of the planet and for most of man’s existence on earth marine resources were viewed as inexhaustible (Hutchings 2000, Sobel and Dahlgren 2004). The last century was marked with an exceptional boom in human populations which put increased pressure on the natural resources to supply our daily food and energy demands. Fishing is seen as a relatively cheap way to obtain a high protein energy source (Bennett and Griffiths 1986a). During the last 50 years, biological studies have measured declines in marine resources and a greater understanding of ecosystem complexity and the life cycles of fish species has prompted conservation concern regarding the sustainability of marine resources (Attwood and Bennett 1995b, Penney et al. 1999, Hutchings 2000, Götz 2005, Cooke and Cowx 2006). In addition to fishing, other extractive forms of resource use, such as bait-collection and shellfish harvesting, are further depleting marine resources (Lombard et al. 2004, Hutchings et al. 2009). Additional impacts include coastal development, mining, pollution, and the introduction of alien species (Lombard et al. 2004).

In 2006 the State of the World Fisheries and Aquaculture reported that 52% of known global fishery stocks were fully exploited, 17% were over-exploited, 7% were depleted and 1% was recovering from depletion (FAO 2006).

Recreational angling has a global impact given that an estimated 11.5% of the world’s population engages in it, landing 10.86 million metric tons of fish annually (FAO 2006, Cooke and Cowx 2006, Mora et al. 2009). The social and economic benefits of recreational angling contribute substantially to local and national economies (Arlinghaus et al. 2002). Effects of angling on near-shore fish populations may be insignificant per unit area, but become significant when multiplied over space and time (Arlinghaus et al. 2002, Mora et al. 2009). Impacts of recreational angling have both direct and indirect impacts, and can limit fish population recovery, reduce inter-specific competition, and change benthic community structure (Attwood et al. 1997b, Armstrong and Falk-Petersen 2008, Granek et al. 2008, Götz et al. 2009a; 13 2009b). Considerable post-release mortality has been documented in catch-and- release fishing, and this is comparable with commercial fisheries by-catch discard (Alverson et al. 1994, Cooke and Suski 2005). Only 13% of the world’s Economic Exclusive Zones gather data regarding catch (FAO 2006). Globally the cumulative impact of recreational angling is now being highlighted as a cause of concern not only because of the biological impacts, but also because of the lack of monitoring.

Monitoring of catch and effort is therefore essential for marine conservation because changes in measured variables can indicate collapse of stocks or ecosystems, and can bring about a quicker response from management (Griffiths 2000, Vos et al. 2000). Although the impact of historical and present commercial line, net, purse- seine and trawl fisheries is quantitatively higher, all sectors of fishing need to be monitored to understand which species and habitats are being targeted over time (Cooke and Cowx 2004). Long-term monitoring can guard against the shifting baseline symptom, and the use of inappropriate reference points from which to evaluate ecosystem condition and economic losses resulting from overfishing. It can also help to identify targets for rehabilitation measures (Pauly 1995). It is important to gather species-specific catch data to know which species are being over-exploited, because overall CPUE is masked when anglers shift their effort from one species to another. This can give the impression of a stable CPUE and sustainability (Griffiths 2000). Monitoring must be structured in such a way that the data gathered can be used to inform resource and conservation managers alike.

2.2 History of monitoring of shore-based fishing in South Africa

South Africa’s linefishery is managed by a single authority, the Ministry of Agriculture, Forestry and Fisheries, although management responsibilities are shared with other agencies, and there is further assistance from quasi-government agencies which are in turn supported by universities, NGOs and museums conducting fisheries research.

In South Africa, declines in galjoen (Dichistius capensis) stocks were observed as early as the 1930s and bag and size limits for certain species were introduced (Smith 1935). These limits, however, were not based on biological studies, thus the regulations imposed were not suitable. Later, in the 1960s, research was conducted on a few commercially important species, and regulations were imposed only for the 14 boat-based commercial sector - little attention was given to shore-based angling (Griffiths 2000). In 1984 an attempt to regulate and restore exploited linefish populations was put in place through an appointed National Marine Linefish Committee (NMLC), by the then Minister of Environmental Affairs. This management framework included revised minimum size limits, daily bag limits, closed seasons, a commercial ban for some species, and a freezing of the commercial effort at the 1984 level. During the 1980s the National Marine Linefish System was established to centralise and standardise data and recording procedures for researchers and authorities. The angler-reported catch card system emerged. Data were analyzed once a year in order to give feedback to management. However, data on shore- based catches (with the exception of KwaZulu-Natal) were limited up until the turn of the century. Declines in catches prompted researchers to study life history patterns of fish assemblages to achieve a better understanding of species responses to exploitation (Brouwer et al. 1997). Only in 2000, 60 years after declines were first noticed, did the Minister of Environmental Affairs and Tourism declare an emergency in the traditional linefishing sector in terms of a provision in the Marine Living Resources Act (MLRA 1998). The emergency status was not only due to declining fish stocks, but also to inappropriate management (Attwood 2008).

Regulations were revised in 1992, 1998, 2002 and 2008 to reduce effort and catches, in an attempt to maintain a sustainable harvest. Currently, shore patrols by law enforcement officials who inspect catch are conducted in limited areas, but monitoring is not conducted, co-ordinated or standardized on a national or local scale by government or conservation institutions.

The introduction of the MLRA in 1998 was viewed as progressive because it promoted a holistic approach by stating that stocks must be managed not only at sustainable levels, but that management must also be consistent with the role that species play within the ecosystem. This legislation should be tested with the use of a standardized monitoring program to see if it is effective in protecting exploited stocks.

South African marine authorities have been unsuccessful in implementing an effective monitoring technique for shore-based recreational angling. The costs involved in employing officers to monitor long stretches of coastline with low-densities of recreational fishermen are deemed too high. The government’s main focus on 15 monitoring has been based on single-species stock assessment techniques applied to commercially viable species (Hutchings et al. 2009). Trends in catches, fishing effort and changes in distribution and abundance of harvested resources have been gathered using fishing-independent and -dependant data. Traditionally, only commercial linefish stocks were measured with the use of per-recruit analysis. Results are interpreted in two ways: (i) they provide relative life-time yield and spawner–biomass of a cohort, and (ii) they give an indication of a population in equilibrium. If the population is in equilibrium at time of analysis the results will reflect current spawner-biomass levels. If not, results will indicate future spawner-biomass levels to be achieved by current fishing mortality regimes once equilibrium is achieved (Griffiths 2000). These calculations also make it difficult to ascertain what the natural mortality rate is or how much of the depletion is attributable to fishing pressure or the accumulative effect of years of depletion on spawner stocks.

Although there is an international trend towards ecosystem-based management, single stock assessments are commonplace in South Africa, with little or no monitoring of multi-species fisheries (Hutchings et al. 2009).

Attwood (2008) highlighted the fact that standard production models are unsuitable for linefish assessments despite their wide application in fisheries. Because there are many species in a fishery of comparatively modest value, the requirements are found to be too costly and government cannot afford monitoring and assessment programmes for each species, as it does for the few highly abundant demersal and pelagic species, which are of high monetary value. Models need to be fitted over several years of monitoring to calculate abundance indices over time. Therefore long-term trends in CPUE are useful for verifying stock status and can be seen as a stock status indicator.

Monitoring with the use of an adequate experimental design, which delivers long- term CPUE time series and size frequencies, will help to determine sustainable extraction rates, as such monitoring can limit or reduce such explanatory variables as long-term environmental change and species catchability (Attwood 2003). Unexploited areas such as no-take MPAs can be used as control sites against which to monitor exploitation. Many empirical studies have been conducted on MPA

16 success, but few have included data of fish assemblage recuperation from before and after closure (Russ and Alcala 1989, Willis et al. 2003).

Management of recreational angling is fraught with problems. Owing to the perceived assumption that recreational fishing does not have a substantial impact (because fewer fish are taken than by individual commercial anglers), monitoring has been sparse, and the protection of recreational linefish species in MPAs has been opposed by some resource users (Birkeland and Dayton 2005). The bag limits in place for some South African species are not particularly effective as they were not based on any scientific evidence and consequently, do not provide any real form of protection. Effective enforcement of regulations along the coastline is also difficult (Attwood and Bennett 1995a, Brouwer et al. 1997, Götz 2005). The commercial and recreational linefishing sector has also been revolutionized by new technology. These include motorized vessels (1930s), monofilament line (1950s), ski-boats (1960s), radios (1970s), eco-sounders (1970s), off-road vehicles (1970s), Global Positioning Systems (GPS) (1990s), cellular phones (1990s), and equipment used for catching (such as thinner, stronger and more transparent lines, rods made from graphite, and chemically sharpened hooks). These factors have made targeting and catching fish more successful. Stocks are still found to be dwindling as effort increases, and as a result anglers are now doing sequential targeting, where they switch from large resident reef fish such as the now heavily-depleted seventyfour

(Polysteganus undulosis) to smaller sparids and shoaling migrants (Penney et al. 1999). Almost all of the species found on the recreational angling list are regarded as exploited and most of these species are no longer allowed to be caught commercially. In KwaZulu-Natal commercial and recreational linefish catch was found to greatly exceed the limit of what the endemic reef fish could sustain (Penney et al. 1999). Natural refuges for fish are fast disappearing. The urban spread along the coastline creates more easily accessible sites, thereby increasing effort.

In 1995 the first real attempts were made to undertake a comprehensive monitoring survey on recreational linefish catches, management and angler attitudes in South Africa. The National Marine Linefish Survey (NMLS) of 1995 stretched over a period of 34 months (1994-1996) where creel and aerial surveys were conducted. The creel surveys covered 19 616 km on foot with over 1 677 patrols, and 9 523 anglers were intercepted (interviewed). The aerial surveys counted 22 609 anglers over 16 497 17 km. Results indicated that angler densities were highest on the KwaZulu-Natal coast (4.65 anglers per km) and lowest on the West coast (0.12 anglers per km). The southern Cape coast was second highest with 2.29 anglers per km. Total effort was estimated at 3.2 million angler days per year and the total catch was estimated at 4 519 914 fish per year, or nearly 3 million kg/per year. Elf (Pomatomus saltatrix) dominated the catch in weight on the KwaZulu-Natal coast (29%), the Eastern Cape coast (26%), and on the Southern Cape coast (56%). Effort was found to be highest around metropolitan areas and over weekends. Of the 250 linefish species recorded, only 12 species made up 90% of the catch (Mann 2000). Knowledge and compliance of anglers regarding the regulations was found to be poor and law enforcement levels were found to be insufficient. The monitoring program highlighted the exploitation level of recreational fish stocks (Mann 2000).

One of the best documented monitoring programs concerning the recovery of surf zone fish species within an MPA has been conducted over a period of 25 years in the De Hoop MPA with the use of fishery-dependant and fishery-independent angling. Monitoring started two and a half years prior and four and a half years after closure of the MPA. After MPA closure to angling, results showed an increase in the CPUE of six species. These made up 97% of the catch and are generally assumed to be the most important angling species in South Africa (Bennett and Attwood 1991). Results showed that resident fish such as galjoen and blacktail (Diplodus sargus capensis) benefited from protection through an increase in population size through recruitment and a steady climb in CPUE. Migratory species such as kob (Argyrosomus spp.) and elf showed no sign of benefit after protection.

Attwood (2003) concluded that the success of a monitoring project lies in the establishment of a reliable monitoring method, using statistically comparable and viable techniques which should cater for the entire fishing industry. The monitoring in De Hoop is based on CPUE and size frequency, as the typical effects of fishing pressure on fish populations is detected by a decrease in mean size and density of target species.

CPUE is influenced by factors other than fish density and these factors need to be excluded before CPUE can be used as an indicator of density (these factors include years, months, seasons, and week days versus weekend days). Ongoing monitoring 18 is thus required to measure the chosen indicators accurately to be useful in assessment of stock status. The standard error in the indicator can set the limit for detection of change. A large sample is also needed to calculate accurate species- specific CPUE (Attwood 2008).

Attwood and Farquhar (1999) suggest that CPUE data over time can provide some assessment of the effect of exploitation, especially when measured between exploited and protected areas. Long-term trends in CPUE are useful for verifying stock status and can be seen as a stock status indicator (Griffiths 2000). CPUE data sets can pick up seasonal fluctuations and serve as an early warning system over time, or can be measured against closed MPAs which share similar habitats, until such time that long-term CPUE data sets have been established (Bennett and Attwood 1993, Attwood and Farquhar 1999).

Other forms of monitoring include on-site and off-site surveying methods where anglers are interviewed and catch and effort data are collected. Tag and release programs and telemetry implants are also used to monitor fish movement and home ranges. These methods are complementary to creel monitoring as they increase management understanding of ecological factors (Attwood and Bennett 1995a, Cowley and Naesje 2004, Kerwath et al. 2007, Thorstad et al. 2009).

Some of the major challenges of monitoring are funding, the employment and deployment of dedicated creel survey staff along the coast, and the costs to maintain, analyze and interpret the data. These reasons lead to data being collected on a sporadic and opportunistic basis. The use of law enforcement staff is undesirable as data collected by these individuals leads to avidity biases and improper sampling as attention is often shifted to areas of high angling effort which results in over estimations of angling effort (Pradervand and Baird 2002, Pradervand and Hiseman 2006).

19 2.3 Review of monitoring methods for shore-based fishing

There are various methods used to monitor shore based angling which is mostly conducted through either fishery-dependent or fishery-independent research. Methods used must be appropriate to meet the objectives required by collecting the right data.

Sampling variability and bias needs to be minimized and sampling must be standardized so that it can be comparable with other monitoring sites. For this study various methods available had to be reviewed which were appropriate for the study site to establish a suitable monitoring method. The associated problems had to be identified and overcome within the framework and budgetary restraints by taking the theory and putting it into a typical South African shore angling situation. The aim of this chapter was to review and look at the feasibility of the various methods and their associated problems.

2.3.1 Methods for monitoring shore-based fishing

Fishery-dependent and -independent data

Fishery-dependent data can be gathered by anglers or researchers but are affected by inaccuracies in angler reports. These data can provide information on catch, effort, gear types, spatial and temporal fishing patterns and can be collected over long or short periods (Pollock et al. 1994, Penney et al. 1999, Pradervand and Baird 2002, Pradervand and Hiseman 2006, Beckley et al. 2008, Hutchings et al. 2009). Long-term data are valuable for understanding the impacts of fishing on the local fish stocks. Data are collected with the use of angler surveys and can be collected on or off site. Owing to biases that result from anglers misreporting their catch, sound survey design is required (Pollock et al. 1994).

Fishery-independent surveys are more precise and representative, as they allow even or randomised distribution of sampling effort over the study area as a whole, and are not affected by inaccurate angler reports. Fishery-independent data are more costly to collect than fishery-dependant data. Methods include, but are not limited to, controlled fishing and mark recapture, and can be conducted in marine 20 reserves as well as open areas. Resulting data can be used to determine movement patterns (Attwood and Bennett 1995b, Cowley 2000), age and growth parameters, and to estimate abundance (Cowley and Whitfield 2001).

Off-site and on-site survey methods

There are seven basic surveying methods which can be used when sampling recreational angling with fishery-dependent methods. They can be classified into two groups, namely off-site and on-site methods (Pollock et al. 1994, pp 65). The four main off-site methods are mail surveys, telephone surveys, door-to-door surveys, and the use of logbooks, diaries and catch-cards. The three main on-site methods are RCS, access point surveys, and aerial surveys.

Off site surveys are conducted away from fishing grounds and usually involve sampling anglers from a list, such as a file of permits. Interviews can be conducted via mail, telephone or door-to-door. Data gathered with the use of catch cards or logbooks can be classified as off-site data, although data may be gathered on site. Off site survey methods are usually used to gather data on angler opinions.

On-site methods are used to measure fishing effort and catch and are employed within the fishing grounds. Information is retrieved from anglers while they are engaged in fishing, or while they are moving to or from their fishing site within the fishing grounds. On-site methods are not biased by self-reported data which rely on the angler’s memory, knowledge and truthfulness. In addition, information can be verified by trained surveyors because the catch can be inspected (i.e. properly identified and measured).

There are five types of on-site surveys. Surveys can be conducted only once or repeatedly using the same sampling units (where units are defined by survey times, areas, and directions): (i) Single-time surveys are used to learn something in particular about a fishery that can not be monitored regularly, or that can facilitate a current management decision. (ii) Repeated surveys with no intended re-use of sampling units are used periodically to track overall annual trends in important fisheries (these surveys 21 are used when trends within individual sampling units are not required, and when sampling of units will be done randomly). (iii) Repeated surveys with partial overlap of sampling units are used to sample larger areas, with limited budgets. Sampling units are rotated at regular intervals and this can reduce the variance of estimates, as well as provide longitudinal data when sampled over time (a longitudinal study is a correlation research study that involves repeated observations of the same items over long periods of time). (iv) Longitudinal surveys with no rotation of sampling units are the classic way to gather longitudinal data over time for set sampling units. This can include surveying the same angler community continually to learn how their attitudes and fishing activities change over the years. (v) Longitudinal surveys with rotation of sampling units follows the same objectives as for (iv) above but with the flexibility of adding new sampling units in over time, and for sampling units to be rotated out.

Sampling frames

A sampling frame refers to the entity being sampled (e.g. anglers on a list, or a particular area). Sampling frames should have an equal probability of sampling all anglers in a target population. In off-site surveys a list frame is used, for example, a list of anglers who purchased a permit, a list of angling club members or a list of registered boat owners. List frames are biased in that they sample only those anglers who comply with the law, and not those anglers who fail to purchase permits, join clubs or register their boats.

On-site angler surveys use area frames, time frames, and area X time frames. An area such as a lake, estuary or stretch of coast is divided into sections and different sections can be chosen for surveys. Thereafter, a type of survey is chosen (from the list of five on-site surveys described above). A time frame is chosen, for example a year, or a fishing season, and this time frame is then subdivided into time units (e.g. week days or weekend days). An area X time frame is the most common on site combination because it combines spatial and temporal frames.

22 Off-site surveys

(i) Mail surveys

Mail surveys are often the preferred off-site method because they are simple and relatively low cost. Mail surveys based on permit holders are mostly used for socio- economic assessments. Random samples are drawn from the available list. ‘Add on’ mail surveys can follow an on-site survey and complement it by gathering more detailed information, especially on the economics of completed fishing trips.

Mail surveys may include a personalized cover letter, questionnaire, postage-paid return envelope, and a reward. After initial letters have been mailed to anglers, second and third letters are mailed to non-respondents. The third mailing scan raises overall response rate from 59% to 72% on average (Pollock et al. 1994). Telephone follow up surveys can also be used for non-respondents, in order to estimate how mail respondents differ from non-respondents, as well as to increase overall response rates. Usually non-respondents are casual anglers, as opposed to serious anglers who usually respond immediately to a survey. These two groups often answer their questions very differently, making it difficult to gather accurate data of the fishery. Pollock et al. (1994) refer to this as a non-response bias, which can be reduced by good survey design, multiple mailings and reward schemes.

A weakness of this survey design is that questionnaires must be very clear, in order to avoid confusion (this problem can be avoided with door-to-door methods or personal interviews). Mail surveys can also take up to 10 weeks, and it is generally accepted that time and quality of response are sacrificed for the benefit of low cost. Another bias that can arise is memory or recall bias, which results when effort or catch is estimated. This is especially true for historical surveys. Results are also affected by the ability of anglers to identify fish species and remember fish lengths and weights with reasonable accuracy, without exaggerating their catches to enhance their image (Pollock et al. 1994, pp 71-83).

23 (ii) Telephone surveys

Telephone surveys are most successful in countries where most households have phones. Consequently, this method is most common in the United States and Canada where 90% of households have phones. Methods used are based on directory frames, random-digit dialing and special registration lists. Directory frames cover angling and non-angling households but do not include unlisted telephone numbers, whereas random-digit dialing consists of all possible telephone numbers which are listed and unlisted and stretch over angling and non-angling households. Special registration lists include fishing permit lists, boat registrations and angling club members. These lists contain only anglers or high percentages of anglers. Telephone survey questionnaires must be easy to understand, and logical. Data are normally entered straight into the computer by the interviewers, who need to be well trained and supervised.

The main strength of a telephone survey is that results are achieved quickly. The time required is minimal when compared with a mail survey, and data are entered almost immediately, coupled with a high response rate. Telephone surveys are useful when night fishing takes place and the safety of the interviewer is of concern. It is a reasonably low cost method compared with personal interview methods. It is also a good method to provide information on angler attitudes and to gather demographic and sociological data.

Directory frames and random digit dialing methods can suffer from under sampling, because directory frames only cover listed numbers, and the distribution of listed numbers is not uniform (it is higher in urban areas). Biases resulting from this type of survey include recall and prestige bias (Pollock et al. 1994, pp 109-122).

24 (iii) Door-to-door surveys

Door-to-door sampling is a very effective off-site survey method because personal interviews are conducted with anglers. This allows for more immediacy, complexity and flexibility. This method is particularly effective in rural areas where telephone and postal services are limited. Because of the high costs of this method, fishery questionnaires are often attached to other broader spectrum questionnaires, for example public opinion surveys on environmental issues.

Door-to-door surveys use either quota or probability sampling. Quota sampling is based on existing information (such as census figures) on the target population and sampling quotas are set for population groups, based on their relative size in the population. Interviewers can choose which individuals to interview and no element of randomness is used. This can bias results substantially, particularly if data from difficult to reach anglers differs a lot from data obtained from easy to reach anglers. Precision of estimates can also not be calculated. This type of sampling is often used when conducting market research.

Probability sampling draws samples from list or area frames, and uses a two-stage sampling method, where areas are divided into subareas, and households are then numbered. A random sample is then chosen. Because many households are non- anglers, the sample size needs to be large enough to be representative of the angling population.

Door-to-door surveys may lead to response and recall bias because they use self- reported data. However, avidity bias is eliminated as non-avid and avid anglers are sampled with equal probability. Non-response bias is also less of a problem than it is for mail surveys. Door-to-door sampling is sometimes the best option in developing countries (Pollock et al. 1994, pp 123-126).

25 (iv) Logbooks, diaries and catch cards

The use of logbooks, diaries and catch cards is classified as an off-site method because data are angler-reported, even though the data are collected on or near the fishing site. Effort and catch data can be obtained. This method is similar to other off-site methods and is thus subject to the same biases.

Logbooks and diaries from angling clubs can be used to provide information on relative abundance of fish populations, and how this changes. Angling club records have been used on South Africa’s east coast to establish long-term trends (Coetzee et al. 1989). This is a relatively cheap way of collecting data although it has been noted that diarists normally have markedly higher catch rates than other anglers. This could be the result of prestige bias or response bias.

The use of catch cards is a good way to obtain data in areas of low fishing effort. Catch cards are submitted on a voluntarily basis and provide data on effort, catch and locality. In South Africa, voluntary catch and effort data are collected with the use of ORI catch cards. Catch cards have slowly been replaced in South Africa by more reliable observer-based inspection data in some parts of the coast. However, catch card returns have been used in several evaluations including the spearfishery and linefishery (Mann et al. 1997, Penney et al. 1999, Pradervand and Hiseman 2006).

26 On-site surveys

(i) Roving creel surveys (RCS)

The RCS is an on-site, intercept design which is widely used to sample recreational fisheries, when catch and effort data are required for specific water bodies. Like the access-point survey (described below), it is a personal contact method (Pollock et al. 1994, Attwood and Farquhar 1999, Beckley et al. 2008).

An RCS is normally used in areas where the coast has many access sites and data on catch rates and fishing effort is required. Surveyors set out according to a pre- determined and randomised schedule, with randomised start times and starting points. Because anglers are intercepted during the act of fishing, and not when trips are completed, total catch is not estimated directly but calculated as the product of effort and catch rates. In addition, anglers who fish for longer periods have more chance of being sampled than those who fish for shorter periods. This results in a length of stay bias. This means that anglers who fish for longer periods have a higher probability of being intercepted, thus the overall mean trip length estimated from a survey will be an over-estimation. There is no current way to avoid or minimise this bias, but the amount of bias will depend on the strength of the relationship between catch rate and fishing trip duration.

The RCS is a very reliable method to use because it is conducted on site and eliminates recall and prestige bias. All anglers are sought out and interviewed. Catch can be inspected by trained surveyors, eliminating incorrect identification and measurement of fish species. The two disadvantages are that data are obtained from incomplete fishing trips, and surveys are costly (particularly when the study area is linear, as opposed to round-trip).

27 (ii) Access point surveys

Like the RCS, the access point survey is an on-site, intercept method. A requirement of the access point survey is that anglers must use few and well-defined access points to enter the fishery. This works well where the majority of anglers use public sites to reach the water (e.g. well-defined parking places), but is not suitable if many private entry points are available.

At access points, anglers’ catches are directly inspected and catch effort and length frequency data are collected (Brouwer and Buxton 2002). This is a retrospective method since data are based on completed fishing trips, as opposed to roving surveys where catch and effort data are collected from incomplete trips. The success of this method relies on a sound knowledge of all access sites in the fishery to avoid over- or under-estimation of effort. Data are less valuable in situations where access sites are overlooked. Because the surveyors are stationary, more equipment can be taken with to capture additional biological data. In general, on-site surveys are more expensive because far fewer interviews are done per hour, as compared to off-site methods. The major costs involved are transportation to sites and surveyor salaries.

(iii) Aerial surveys

Aerial surveys are used when data on angler effort is required over large areas. Although there is no personal contact with anglers, anglers are counted while in the act of fishing, so this is categorized as an on-site method. A small fixed-wing airplane is used and can cover 800 km in four hours, thus covering many fisheries. When covering shorelines, anglers on foot are counted as individuals and boats are counted as fishing parties. As the plane flies over the area, surveyors make instantaneous counts but the overall survey count is progressive. Instantaneous counts are made from a vantage point and are done quickly, progressive counts are used on larger areas where counting is done over a substantial amount of time. Aerial surveys are often conducted to monitor effort over weekdays, weekend days and public holidays.

28 The present study

An on-site, rather than an off-site, method was decided upon for this study because on-site methods have a lower potential for non-response, and sampling errors than on-site methods (Pollock et al. 1994). The roving creel survey was chosen over the access-point survey because there are too many potential access points in the MPA. Owing to budget constraints, an aerial survey would be too costly. A full description of the roving creel survey is given in Chapter 3.

29 Chapter 3 Methods: the roving creel survey

3.1 Background

The RCS method was chosen for this study because the Goukamma MPA has many access sites which can not be surveyed by stationary surveyors (Figures 5 and 6). The RCS is an on-site intercept method: anglers encountered were interviewed and catch was identified to species level and measured. Estimated catch, fishing effort, CPUE and spatial and temporal angler densities were then calculated from interview data. Anglers encountered were asked what time they started fishing and how many fish they had caught up to the time of the interview. With this method, good estimates of fishing effort can be obtained in low angler-density fisheries because all anglers encountered are interviewed. Because anglers are still in the process of fishing, an RCS obtains data from incomplete fishing trips. Two key assumptions underlie the estimation of catch rate when extracting data from incomplete fishing trips: (1) the catch rate (fish/hour) up until the time of the interview will equal the rate for the entire fishing trip; and (2) the catch rates of interviewed anglers are equal to those of non- interviewed anglers. These assumptions are important as total catch is calculated by multiplying the catch rate by the estimated angler hours (obtained from angler day lengths per month).

Anglers’ catches are inspected and measured to obtain length frequencies. This allows us to plot time series data, which in turn allows us to determine how the individual fish populations in the MPA are responding to angler effort. Although no law enforcement is undertaken by surveyors in the RCS, information can be gathered on angler response to minimum size limits and daily bag limits.

The Goukamma study used the same sampling method (i.e. catch, effort and CPUE) and statistical analyses as the fishery-independent monitoring that is undertaken in the no-take MPA of De Hoop. This facilitates a comparative analysis between Goukamma MPA and De Hoop (which is also in the Agulhas Bioregion). Different fishing methods, however, are used at the two sites. In De Hoop standard methods (i.e. hook size, gear, time spent fishing) are used by skilled anglers (Bennett and

30 Attwood 1991, Attwood 2003) whereas in Goukamma many different types of fishing gear are used by anglers of various levels of skill and age.

During August-December 2008, a pilot RCS was undertaken and 22 surveys were completed. Results from these surveys provided the basis for the spatial and temporal stratification for the 2009 surveys and gave us the opportunity to identify and minimise bias. A total of 166 surveys was conducted (2008 -2009) and the Goukamma MPA was divided into two sampling sections (Groenvlei - 11 km and Buffalo Bay 4 - km, Figure 2). Within each section, 12 surveys were undertaken each month in 2009. Sampling was done in a randomly stratified manner with a 1 week day to 3 weekend day ratio (all public holidays were treated as weekend days and from here on, ‘weekend day’ refers to both weekend days, and public holidays). This ratio allowed us to sample 71 % of the fishing effort (using the method described in Pollock et al. 1994, pp 167).

No attempt was made to document night time fishing within the Goukamma MPA, although such activity was noticed throughout the study. During early morning surveys some anglers were observed leaving with overnight gear, although few would admit to staying the whole night (anglers are not permitted within the reserve between sunset and sunrise). Evidence of night fishing was often observed (for example fires, sleeping places, tracks where bags were dragged over many kilometers, and vegetation which was used for firewood).

Spatial density analysis of effort per habitat type was calculated by obtaining a GPS location of each angler party encountered and plotting these positions in a Geographic Information System (GIS, ArcView 3.2a). Measurements of all fish were taken to determine species population age.

Stratified random sampling was used in the design of the RCS. The selection of sampling times and places was done by selecting firstly the date, then the starting time within the day, and then the starting position of the survey (Pollock et al. 1994).

31 3.2 Week day versus weekend day sampling ratio

The first five months (August – December 2008) of the study were exploratory and were used to test the logistics of the method, and to stratify sampling effort. Twenty- two surveys were completed, on both week days and weekend days (this was the maximum number that the study could afford at that time). The method on pp. 176 of Pollock et al. (1994) was used to determine the ratio of week days to weekend days that would sample the greatest fishing effort. There were no existing data on how fishing effort was distributed over day types so it was assumed that effort was split according to the proportion in which day types naturally occur (i.e. 5 week days to 2 weekend days) and the 22 surveys were apportioned accordingly. However, the pilot study showed that there was very little effort on week days, and much more effort on weekends (Table 2, Step 1). On average, 1.8 anglers were encountered on any week day versus 15 anglers on any weekend day.

Table 2. Distribution of the number of anglers encountered on week days (WD) and weekend days (WE) during the five month pilot study (Step 1). Step 2 shows how these data were used to apportion the 12 surveys per month that the study could afford for the 2009 survey period.

Step 1 Step 2 Data from WD WE WD WE Total 22 surveys Sample a Sample g Mon 1.8 Sat 15 b h Tues 1.8 Sun 15 c i Wed 1.8 d j Thurs 1.8 e k Fri 1.8 f l Average no. 1.78 15 of anglers per day

Total no. of 8.9 30 38.9 anglers per week Ratio 22. 70. 100 9 1 Ratio scaled to 12 2.7 9.2 12 5 5 Rounding 3 9 12

32 We could afford 12 surveys per month for 2009 and wished to sample week days and weekend days according to the distribution of fishing effort, rather than the distribution of day types. Consequently, based on the data in Step 1 (Table 2), we apportioned the 12 surveys according to Step 2 (Table 2), i.e. three week days per month, and nine weekend days per month.

Using the method on pp. 176 of Pollock et al. (1994), it is possible to determine how much of the total fishing effort is being sampled with this survey design. Table 3 shows that 12 monthly surveys, apportioned as 3:9 week days: weekend days, allows us to sample as much as 71% of the monthly fishing effort. Figure 7 shows how other ratios and numbers of surveys per month affect the % of monthly effort sampled.

Table 3. Estimated monthly effort sampled using the 12 surveys a month, apportioned as three week days and nine weekend days.

Week days Weekend days Total No. days in month 21 10 31 No. of days to sample (apportioned according to % fishing effort) 3 9 12 % of sampling effort 25 75 100 Sampling covers X% of day type 3/21%=14.3 9/10%=90.0 If fishing effort (%) ratio is: 25 75 100 This covers X% of monthly fishing effort 14.3/100*25=3.6 90/100*75=67.5 71.1

33 100 d e l 90 p m

a 80 s

t

r 70 o f f 60 e 1 WD : 1 WE g

n 50 i h

s 40 1 WD :2 WE i f

y l 30 1 WD :1 WE h t

n 20 o m

10 % 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Number of surveys per month

Figure 7. Comparisons of different week day (WD): weekend day (WE) sampling ratios, and different number of surveys per month, with respect to the total fishing effort sampled.

As previously mentioned, a stratified random sampling method was used to allocate survey days, starting times, and starting positions in the study area (see Excel spreadsheet in Appendix 1). Five tables were created, with the following headings: 1. Choosing week days in a month 2. Choosing weekend days/public holidays in a month 3. Choosing start times for the Groenvlei section 4. Choosing start times for the Buffalo Bay section 5. Choosing start positions This method of sampling design mimics ‘drawing out of a hat, without replacement’ (replacement is not possible because once a survey is allocated to a particular day and time, another survey cannot occur at that same day and time).

34 3.3 Questionnaire

Correct questionnaire construction is extremely important to ensure successful analysis of data collected. A questionnaire is called the survey instrument and can be executed on-site, off-site, face to face, by telephone or mail. As previously described, all collection of data for this study was done on-site, face-to-face, in order to minimise recall and prestige bias (Pollock et al. 1994, pp 49). All questions were designed to contribute to answering the survey objectives. Wording was clear and unambiguous, with an attempt to elicit the most accurate answers. Questions were ordered and grouped to promote interest, as opposed to irritation, because many anglers in this area were known to be uncooperative in the past.

Each survey conducted used a cover sheet where general information was noted, for example the survey number, date, total number of anglers and weather conditions. The following conditions were recorded: • Sea temperature (using a thermometer at a pre-determined area) • Wind direction at the study site • Wind speed, measured in metres per second, from the website Windguru (www.windguru.co.za) • Swell (flat, average or rough) • Cloud Cover (clear = blue skies with a few clouds; partly cloudy = 40 % of the skies are covered with clouds; overcast = >40% cloud cover; overcast and rainy = overcast and raining) • Moon phase (from Navy charts – phase closest to survey date was chosen)

All questions relating to catch are shown in Appendix 2.

35 3.4 Effort, Catch and CPUE 3.4.1 Background To measure annual angler effort (angler-hours) in the MPA, the average number of anglers per week- or weekend day, per month, was determined, as was the number or hours in monthly fishing days. According to Pollock et al. (1994) angler effort should not be determined from interview surveys because time is lost during interviews and this introduces a bias towards underestimation of fishing effort. In order to eliminate this bias, two people were used during initial surveys, one to count anglers (the ‘zoomer’) and another to stop and interview anglers (the interviewer). The ‘zoomer’ and interviewer started the survey together, but the ‘zoomer’ did not stop for any interviews.

During these initial surveys, effort was measured using both instantaneous and progressive counts. Instantaneous counts are made from a vantage point and are done quickly (within 15 minutes), whereas progressive counts are undertaken in larger areas where counting can take much longer (normally more than an hour). Instantaneous counts were used by the ‘zoomer’ in the Buffalo Bay section, because a vehicle could be used on the access roads and the survey took less than 15 minutes. Counts were done from pre-determined vantage points noting the position and numbers of anglers (Pollock et al. 1994). Progressive counts were undertaken by the ‘zoomer’ in the Groenvlei section because this area covers over 12 km of coastline inaccessible to vehicles.

Instantaneous counts are unbiased as long as no anglers are missed owing to visibility problems, and if anglers can be distinguished from non-anglers. If anglers are not visible or if they are mistaken for non-anglers; fishing effort will be underestimated. Conversely, if non-anglers are counted as anglers, effort will be overestimated. For this reason, an angler is defined as any person changing tackle, moving between sites with rods, or actually engaged in fishing. It is also important to compare the number of rods with number of people (Pollock et al. 1994).

Both study area sections were surveyed on weekends but most weekdays surveyed only one section. Owing to the size of the areas and budgetary constraints, within-day variability could not be sampled, only between-day variability. To achieve consistency and to minimize bias, a standard walking pace was implemented, as well as ¼, ½ and 36 ¾ check points to be reached at certain times for both the zoom and interview person (although each survey length was influenced by environmental conditions such as tide, wind, weather and number of anglers encountered).

A total of 49 initial (trial) surveys were conducted over seven months, involving both a ‘zoomer’ and interviewer, in order to determine if the method of survey affected the number of anglers encountered. The following hypothesis was formulated:

Ho = There is no difference between the number of anglers encountered by the ‘zoomer’ and the interviewer

H1 = There is a difference between the number of anglers encountered by the ‘zoomer’ and the interviewer A t-test assuming equal variance was used to analyse the data. A mean difference of 0.6 anglers (Stdev 2.84 anglers) was measured in favour of the interviewer (interviewer intercepted more anglers) but this difference was not significant at the 95% level. Consequently, I concluded that there is no significant difference in angler counts between the two methods and the ‘zoomer’ was dropped for the remainder of the study.

A total of 329 surveys were completed from August 2008 – December 2009 (Table 4). During the 2008 trial study, the entire MPA was treated as one section, but during the 2009 surveys, for logistic and statistical reasons, we split the study area into two sections (Figure 2): Buffalo Bay, a 6km long stretch of coastline was surveyed from a vehicle, and Groenvlei, a 12 km coastline was surveyed on foot (no vehicle access). On average, 24 surveys were conducted each month (12 in each of the two sections). Surveys were completed by the author (Carika van Zyl) and Roland Scholtz.

37 Table 4. Number of roving creel surveys conducted in the Goukamma MPA for the period August 2008 – December 2009.

Month Buffalo Bay Groenvlei Week Week- Total Week Week- Total Grand day end day end Total 2008 August 2 1 3 2 1 3 6 September 3 1 4 3 1 4 8 October 2 1 3 2 1 3 6 November 3 3 6 3 3 6 12 December 2 3 5 2 3 5 10 Sub total 12 9 21 12 9 21 42 2009 January 3 9 12 3 9 12 24 February 3 8 11 3 8 11 22 March 3 9 12 3 9 12 24 April 3 10 13 3 10 13 26 May 3 9 12 3 9 12 24 June 3 9 12 3 9 12 24 July 2 8 10 3 8 11 21 August 3 10 13 3 10 13 26 September 3 9 12 3 9 12 24 October 3 9 12 2 10 12 24 November 3 9 12 3 9 12 24 December 3 9 12 3 9 12 24 Sub total 35 108 143 35 109 144 287 Grand Total 47 117 164 47 118 165 329

Data from questionnaires for each survey were entered and stored in a Microsoft Access relational database designed by Colin Attwood and the author (Appendix 3). Queries were built in Access to group variables which determine Catch, Effort and CPUE. Equations were also built into the Access queries to ease further analysis, by using the ‘Expression Builder’. This helps when working with large data sets, and also minimizes error. Any new data entered automatically updated the queries. Data based on Access queries were imported into Excel and Statistica© where further analyses could be undertaken with pivot tables and GLMs, respectively.

The mean, standard deviation and standard error of the CPUE of the dominant species in the catch were calculated per species, section, month and year. Angler counts were converted to predicted daily effort (in hours) by multiplying by the length of the angling day, which varied between months:

38 Emz = eˆmz hm Equation 1 Where:

Emz = predicted daily effort in month m and section z, eˆmz = average angler counts in month m and section z, and hm = day length in month m.

The length of the day in each month was the difference between sunrise and sunset on the 15th of each month.

Total effort over each season per section was determined by multiplying the predicted daily effort by the number of days in the month: Total Emz = Emz dm Equation 2 Where: Total E mz = Total effort in month m and section z, and

d m = days in month m.

In each month the average CPUE was calculated using: CPUE = Ci i ti Equation 3 Where:

CPUEi = Catch per unit effort of interview i.

Ci = catch in interview i, and ti = time of interview minus time started fishing for interview i.

Units of CPUE are fish numbers per hour. The CPUE data were averaged for each month and each section. Paired T-tests assuming equal variances were tested on CPUE of species between the two sections using the hypothesis:

Ho = There is no difference between the CPUE of species in the Groenvlei and Buffalo Bay sections

39 H1 = There is a difference between the CPUE of species in the Groenvlei and Buffalo Bay sections

The CPUE values of De Hoop and Goukamma were also compared for those species with available data.

To measure the variability of species-specific CPUE between months within each section we used the Variance (s2) function in Excel®.

The Coefficient of variation was calculated as follws:

σ CV = ∗100 % χ Equation 4

Where:

CV = Coefficient of variation

σ = Standard deviation

χ = Average

Total catch estimates per month were the product of the CPUE and effort estimates: Total Cmz =CPUEmz x E mz Equation 5 Where:

Cmz = Total catch in month m and zone z,

CPUEmz = average catch per unit effort in month m and zone z Total E mz = Total angler effort (hours) in month m and zone z.

40 A Generalised Linear Model (GLM) using a Poisson Link function was used to calculate average angler number per day per month over each area.

( β o + β1M + β 2WDWE + error ) A = e Equation 6 Where:

A = average angler numbers per day type over month β = represents the coefficients, and M the month and WDWE the week day or weekend variable.

41 3.4.2 Length frequencies

In order to assess species-specific stocks, fished species need to be measured and aged as the size and age structure of a fish population is a record of its recent past history (Damm 1987). Growth indicates the change of body over time and is dependant on the availability of food. Size classes fluctuate during periods of recruitment and seasonal fluctuation in growth due to temperature change has been documented (Gayanilo and Pauly 1997). The size-frequency distribution of a population contains information on growth, mortality, recruitment, and within and between year variations of these attributes. Methods used include attributing ages to peaks in mean sizes (Petersen method) and linking the peaks of the length frequency distribution (modal regression analysis). Large sample sizes are needed to determine these parameters. From these, parameters describing the mean attributes of a population can be derived. Length measurements commonly used include fork length (FL), total length (TL), or standard length (SL). For this study we used fork length (to the nearest millimetre) because most reef fish found in Goukamma have forked tails and because this measure is more reliable that total length. Fork lengths were then converted to total length (Table 5). This eliminates errors caused by damage to the ends of fish tails. Total length can be used to determine age and mass, and these values assist one to determine the state of the stock (Jennings et al. 2009). However, there will always be errors associated with length parameters: these could arise from measurement errors or because the measured fish population is not representative of the local population. The effects of error can be rectified by using sensitivity analysis such as Elefan (Electronic Length Frequency Analysis) (Gayanilo and Pauly 1997).

Size distributions of four numerically important species were compared with the size distribution of the same species at De Hoop MPA. This study does not attempt to describe stock population through length frequency data, but lays the foundation for future data collection.

42

Table 5. Length frequency equations used for the numerically important species. Species Equation Reference Blacktail Mann and Buxton TL(mm) = .626 FL(mm) + 2.554 (Diplodus sargus 1997 (In Mann 2000) capensis) Bennett and Griffiths Galjoen TL (mm) = FL(mm)/0.091 -2.71 1986b (In Mann 2000) (Dichistius capensis)

van der Walt Strepie TL(mm) = 1.149FL(mm) +1.14 1995 (In Mann 2000) (Sarpa salpa)

Elf TL(mm) = FL (mm) – 1.275) / 0.8976 Attwood (pers comm.) (Pomatomus saltatrix)

Marais and Baird Cape stumpnose TL(cm) = -0.218 + 1.368 SL(cm) 1980 (In Mann 2000) (Rhabdosargus holubi)

Belman Mann 2000 (Umbrina robinsonii) Unknown

43 3.4.3 Spatial analysis

The geographic data set of angler parties encountered was compiled with a Garmin GPS; co-ordinates were also documented on the survey sheets. The intertidal shore classification was based on Clark and Lombard (2007) but some changes were made based on finer scale information gathered during surveys.

All spatial data were plotted and analyzed with the GIS Arcview 3.2a (Environmental Systems Research Institute, Redlands, California) and Excel. GPS co-ordinates were downloaded from the GPS into Excel and imported into Arcview in decimal degrees (dd) on a WGS 84 spheroid. Data were projected into a Transverse Mercator projection (Longitude of origin 23) to match the projection of orthophotographs for the area. The coastline of the study site was digitized from these photos. Owing to the fact that anglers are not stationary and move along the coast, it was not possible to give location specific catch information. However, given that anglers typically spend a far greater percentage of their time fishing than moving (personal observations), it was possible to determine if they favored any particular habitat type. Because the different intertidal habitats are of different lengths, the number of anglers per km of habitat was calculated. A Chi square test was used to compare angler densities per km of habitat.

In order to plot a map of angler density for the entire MPA, the coastline was divided into 100 m sections in the GIS as follows: points at 100 m intervals were generated with the ‘divide line by adding points evenly’ tool. Angler localities were then matched to the closest point with the ‘Geoprocessing’ tool. Each point then showed the total number of anglers associated with it over the entire study period.

44 3.4.4 Comparison of catch composition among three data sets and diversity index

Three data sets were used to compare catch compositions between Goukamma and the De Hoop MPA between the time periods within each site. These included ten years of law enforcement creel type surveys and catch card contributions, from 1993 to 2003 (Maggs 2010) for Goukamma; fishery-independent data from De Hoop (2000-2010); and the 2008-2009 RCS data from the present study. Catches were grouped into years for each site. Species composition data were standardized, and root-root transformed (to incorporate rare species), using the Primer Statistical package version 5.2.9 (Clarke and Warwick 2001). A resemblance matrix was constructed using Group Average Linkage. A multi dimensional scaling plot was used to explore similarities and dissimilarities among the catch data by providing a geometrical representation of their relationships (Primer version 5.2.9 software). A Simper output was produced to see which species cause the main differences in catch composition among the three samples. This output tells you what the most common species is in each data set, and which species differentiate pairs of groups. Because the present study had only one year of data, I compared the early Goukamma data of Pradervand and Hiseman (2006) with the De Hoop data.

45 Chapter 4 Results and Discussion: Angler effort

4.1 Temporal distribution of anglers

This chapter presents the results on the spatial and temporal effort found within the MPA. The 329 surveys done during October 2008 – December 2009 resulted in 1242 anglers being encountered within the MPA and 104 surveys resulted in zero anglers encountered. More anglers were encountered in Groenvlei (n = 759) than Buffalo Bay (n = 483). Figure 8 shows the average number of anglers per month in the two sections of the Goukamma MPA encountered during surveys.

Angler numbers were highest in the Groenvlei section with peaks in the winter months (March-August) when the galjoen season is open. The average number of anglers varied from 1 to 8 per day. In both sections, angler numbers were highest in July and lowest in November.

14

s 12 r e l g

n 10 a

f o

r 8 e GV b

m BB

u 6 n

e g

a 4 r e v

A 2

0 Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Figure 8. The average number of anglers encountered per month during 2009 in the Groenvlei (GV) and Buffalo Bay (BB) sections of the Goukamma MPA. Bars indicate one standard error and Y axis average number of anglers. 46

Buffalo Bay had no anglers in February, April, September and October during week days, and Groenvlei had no week day anglers in October and November.

Based on the number of anglers encountered over day type and month, a GLM was fitted to the effort data to determine the effects of month and day type on the variability of effort, separately for each site. The GLM also estimated average numbers of anglers over month and day type, the fitted values are displayed in Figure 9 and 10.

47 4.1.1 Estimated angler averages - Buffalo Bay

Week-day effort in Buffalo Bay is lower than weekend effort as seen in Figure 9. Angler numbers peak in the summer months from October to December during both day types. Angler numbers were constant (n = 3) during weekdays for the first half of the year and steadily increased from October. The lowest number of anglers (n = 8) was estimated for March and July over weekends and July (n = 2) for weekdays. The annual average number of anglers within the Buffalo Bay section was estimated at n = 1959.

14 s r

e 12 l g n

a 10

f o

r 8 e WE b

m 6 WD u n

e

g 4 a r e

v 2 A 0

n r r y e y t v c b a p a l g p c o e Ja e A n u u e O F M M Ju J A S N D

Figure 9. GLM-predicted average number of angler counts through the year, and by day type, at Buffalo Bay. WE = weekend days and WD = week days.

4.1.2 Estimated angler averages - Groenvlei

Week day numbers of anglers in Groenvlei are again much lower than weekend angler numbers (Figure 10). January had the highest number of anglers (n = 13) during weekends followed by February and November (n = 11). Weekday effort in angler numbers were mostly constant (n = 3) but peaked in the summer months from January to February. The annual estimated average number of anglers at Groenvlei was 1703.

48 14 s

r 12 e l g

n 10 a

f o

r 8 e WE b

m WD

u 6 n

e

g 4 a r e v

A 2

0

n r r y e y g p t v c b a p a l c o e Ja e A n u u e O F M M Ju J A S N D

Figure 10. GLM-predicted average number of angler counts through the year, and by day type, at Groenvlei. WE = weekend days and WD = week days.

Table 6. GLM results of the effect of month and day type on angler effort in the two sections of the MPA. Groenvlei df χ 2 Chi- p Effect Square Month 11 -605.74 162.82 < 0.00001 WD_WE 1 -532.4 146.67 < 0.00001 Month X WD_WE 10 -515.87 33.068 < 0.00001 Buffalo Bay df χ 2 Chi- p Effect Square Month 11 -449.05 49.771 < 0.00001 WD_WE 1 -413.62 70.855 < 0.00001 Month X WD_WE 7 -376.09 75.062 0.0003

Table 6 shows the GLM results for the effect of month and day type on angler effort within the Groenvlei and Buffalo Bay sections. Angler effort is significantly different (P<0.001) between different months, between weekdays and weekends, and between weekdays and weekend in the different months, in both sections of the MPA.

49 4.2 Estimated angler hours

Table 7 and Figure 11 show the estimated monthly effort expressed as hours fished within the two sections of the MPA. A total annual effort of 21 842 hours within the entire MPA was predicted with a mean trip length of 6 hours. Highest effort was in July in both sections. November has the lowest total effort in the MPA. Groenvlei effort was much higher than Buffalo Bay effort, with a monthly average of 1089 hours. The Buffalo Bay monthly average was 731 hours.

Table 7. Estimated monthly effort expressed as hours fished per section within the Goukamma MPA. Monthly effort Month Buffalo Bay Groenvlei Total January 1520 1319 2839 February 338 893 1231 March 433 1742 2175 April 254 1074 1328 May 568 1409 1977 June 822 1167 1989 July 1960 1853 3813 August 618 1283 1901 September 540 1008 1548 October 442 324 766 November 333 342 675 December 950 650 1600 Total 8778 13065 21842 Mean 731 1089 Minimum 254 324 Maximum 1960 1853 Std Dev 520 485

Groenvlei had the most fishing effort in hours fished over all four seasons, but that it was not significant (P > 0.05). Angler effort is bimodal in Buffalo Bay with peaks in winter and summer. July and January had the highest effort for this section.

50

2500

2000 d e h s i f

s 1500 r

u GV o h

BB y

l 1000 h t n o

M 500

0

n r r y e y g p t v c b a p a l c o e Ja e A n u u e O F M M Ju J A S N D

Figure 11. Predicted numbers of hours fished, per month, in the two sections of the Goukamma MPA. Bars indicate one standard error.

51 4.3 Spatial distribution of anglers

4.3.1 Angler density per habitat type

Table 1 and Figure 4 in Chapter 1 show the lengths of different intertidal habitats in Goukamma. The analysis of number of anglers per habitat type shows that there is a significant difference in habitat utilization and most anglers encountered were not randomly dispersed but favored the very small, accessible habitats in the Buffalo Bay section (Figure 12 and Table 8). Within the Groenvlei section, more anglers were encountered per km on the mixed shores (scattered rock and sand) than on the sandy shores. Density was least at the estuary mouth.

250

f o 200 m k

r e p

150 s t r a e t l i g b n a 100 a h

f o

r

e 50 b m u

N 0 Estuary Rock exposed Sand Scattered rock Rock Very Broken rock Sand above temporarily (intermediate) and sand exposed and sand rock exposed closed exposed Habitat

Figure 12. Number of anglers per km of intertidal habitat in the Goukamma MPA during the study period.

Table 8. Results of a Chi square analysis of anglers encountered within the seven different intertidal habitats. Chi square 12168.0508 df 6 Chi square critical value (0.05) 12.592

52 4.3.2 Angler density per 100 m section

Figure 13 shows that angler density per 100 m interval in the Groenvlei section gradually decreases from the two access points (in the West and East) towards the centre of this section. Very few anglers were intercepted on the 5 km sandy shore between Skimmel Krans and the estuary mouth.

Even though Buffalo Bay had a lower fishing effort (hours) than Groenvlei, the angler densities per 100m interval were higher than those of Groenvlei. The sand above rock habitat is the smallest habitat in the MPA but the closest rocky area to the very popular Buffalo Bay Beach so access is easy. As mentioned in Chapter 3, anglers are not stationary and move along the coastline. Interview information was gathered as soon as an angler was encountered, thus the angler could be stationary and fishing, or moving from one place to another. However, given that anglers typically spend a far greater percentage of their time fishing than moving (personal observations), we are reasonably confident that the results presented reflect real trends.

Platbank ### ###### # ######### ###### ##### # ##### ##### ####### Garries Bank ###### ##### ##### Oysterbeds #### #### Buffalo Bay ### Skimmel Krans ### ### # ########## # ###### ##### Rowwe Hoek Angler density per 100m # 1 - 6 Walker Point per# 100m7 - 12 # 13 - 18 N # 19 - 28 # 29 - 39 W E

S Figure 13. Angler density per 100m interval along the entire length of the Goukamma MPA during the study period (orange lines indicate access points).

53 4.4 Discussion

The GLM gave estimates on average number of anglers over the different months and day types from which we could equate annual effort estimates. Monthly effort over the year varies according to season and section which is a reflection on angler response towards closed seasons and holidays.

The significant difference between months and day types within the MPA can be explained by the fact that the MPA is not situated in close proximity to any large towns or industry. According to Brouwer et al. (1997) angling effort is denser around metropolitan area giving rise to more access sites. This could also partially explain why anglers are more densely distributed in the Buffalo Bay section as it is a built up environment with a road structure and many access sites to the fishery (Figure 6). Groenvlei has only one main access site on the Western boundary of the reserve and an access point 11km further on the Eastern side at the Goukamma River. This leads to the MPA being frequented over weekends and school holidays on a recreational basis and is reflected in the bimodal distribution of the Buffalo Bay effort in hours fished and higher weekend angler counts. A small resident community accounts for most of the numbers of anglers outside of holiday time. Individuals from this community were often observed fishing during the surveys. Most fishermen who were encountered on a regular basis over weekdays in the Groenvlei section were retired. Sunday afternoons in Buffalo Bay were often observed as a favorable time for fishing, although effort per angler was low (i.e. fishing trips were short). The sand above rock habitat in Buffalo Bay was the most popular fishing area, but is also the smallest intertidal habitat within the reserve and the closest to the main beach at Buffalo Bay.

Groenvlei was fished more in hours than Buffalo Bay. Angler effort in hours peaked over the autumn and winter months for Groenvlei. . The main reason for this is that Groenvlei is inaccessible to vehicles (except for the two access points at each end of the section). This section is a favored galjoen angling area and it is interesting to note the increase in effort in March when the galjoen season opens for the year. Although Groenvlei had a lower density of anglers per 100m than Buffalo Bay, angler effort in hours was significantly higher. Highest densities in Groenvlei were noted over the first 2 km at the Platbank entrance, which is good fishing areas (i.e. mixed

54 shores) as well as being close to the main access site. Lower densities were noted at Oysterbeds, near the centre of the Groenvlei section. This is a prized fishing area, but requires stamina to get there because the walk is treacherous at high tide, especially when the sea is rough. Many anglers were unable to scale the rocky outcrops with their gear and would stay till the low tide would come in again thus most anglers found at Oysterbeds would stay the whole day. The sandy shores that cover about half of the MPA had the lowest densities of anglers. Throughout the surveys, there were stretches of coast between Skimmel Krans and the river mouth where anglers were not encountered. This is most likely because of the six kilometer walk along the sandy shore (from the eastern access point) before the desired rocky platforms are encountered. Overall, angler density was highest where good fishing habitats (mixed shores) and easily accessible areas correlated, such as the first 2 km of the Platbank entrance and Buffalo Bay. To improve confidence, future surveys should note that fishing versus moving activity of any angler interviewed.

Angler parties in the MPA consisted mostly of individuals or small groups of two or three individuals. Parties with more than three individuals were seldom encountered.

The significant difference between day types and the fact that most angling effort was found to be expended over weekends corresponds with the findings of the South African shore angling survey (Brouwer et al. 1997). This was also found in the RCS done in Richards Bay harbor, the study the 40 % distribution of weekday effort to anglers living and working in close proximity to the harbor (Beckley et al. 2008).

In comparison the Pradervand and Hiseman (2006) study found that monthly distribution of fishing effort apart from spring and early summer was relatively uniform. Differences in weekday and weekend effort were found to have less than 6 % variation. This study found that there were a highly significant difference between months and day types. Owing to the fact that data were based on catch card returns and opportunistic patrols by CapeNature’s Conservation officials’ annual effort and catch estimates could not be determined, due to error in sampling. Spatial and temporal data were either lacking or not present, thus areas of high effort could not be estimated.

55 The surveys did not cover night fishing, and our estimates of fishing effort are likely to be an underestimate. Brouwer et al. (1997) found that 54 % of anglers interviewed during their study admitted to night fishing, so it is more than likely that this activity is taking place on a regular basis within the MPA.

The difference in estimates highlights the importance of monitoring programs to follow a uniform stratified random sampling procedure covering all months and day types in the correct ratios. Results show that the design of this survey was strong enough to prove that angler effort is not uniform in the MPA over the year, but strongly influenced by season, months, day types and holidays. Results further show that angler behavior targets certain habitats more than others and this was found to be significant. From this I conclude that the smallest habitats are significantly targeted more through angler effort which could lead to the over-exploitation of certain species.

56 Chapter 5

Results and Discussion: Catch composition, catch per unit effort and total catch

This chapter presents and analyses data from the 2008-2009 RCS conducted in the Goukamma MPA. The survey was undertaken to initiate a monitoring program than can supply vital data for the assessment of fish populations in the conservation area.

Data from the 2008-2009 RCS have enabled a calculation of catch composition, CPUE, catch estimates, and a frequency distribution of catches per species by shore anglers in the Goukamma MPA. This information has been used to answer the following questions: 1. What is the catch composition, by area and by month? 2. Has the catch composition changed since the Pradervand and Hiseman (2006) study? 3. Is there a similarity between the catch composition of De Hoop MPA and Goukamma MPA? 4. Is there a difference in diversity between the Groenvlei and Buffalo Bay sections? 5. What is the size composition of the species caught? 6. What is the species-specific CPUE of the numerically important species? 7. What is the average size of the catch in Goukamma and De Hoop?

5.1 Catch composition

Catch data were recorded for fish that were retained, released or tagged. From a total of 361 fish caught, 336 were retained and the number of fish caught for each of the 23 retained species per area is shown in Table 9.

57 Table 9. Number (and percentage) of retained fish, per species, in the two sections of the Goukamma MPA (October 2008 – December 2009). Buffalo Bay Groenvlei Total Scientific name Common name No. % No. % No. % Diplodus sargus Blacktail 30 48.39 193 70.44 223 66.37 capensis Dichistius capensis Galjoen 3 4.84 35 12.77 38 11.31 Rhabdosargus holubi Cape stumpnose 4 6.45 7 2.55 11 3.27 Umbrina robinsonii Belman 2 3.23 9 3.28 11 3.27 Sarpa salpa Strepie 7 11.29 2 0.73 9 2.68 Pomatomus saltatrix Elf 4 6.45 4 1.46 8 2.38 Argyrosomus japonicus Kob 0 6 2.19 6 1.79 Pomadasys Grunter 0 5 1.82 5 1.49 commersonnii Lithognathus mormyrus Sand steenbras 0 4 1.46 4 1.19 Chrysoblephus laticeps Red roman 3 4.84 0.00 3 0.89 Epinephelus Yellow belly 2 3.23 1 0.36 3 0.89 marginatus rockcod Charcarias taurus Raggedtooth 1 1.61 1 0.36 2 0.60 shark Pachymetopon grande Bronze bream 2 3.23 0 2 0.60 Sparodon durbanensis Musselcracker 2 3.23 0 2 0.60 Triakis megalopterus Spotted gully 0 1 0.36 1 0.30 shark Cymatoceps nasutus Poenskop 1 1.61 0 1 0.30 Dichistius Banded galjoen 0 1 0.36 1 0.30 multifasciatus Diplodus cervinus Zebra 0 1 0.36 1 0.30 hottentotus Gymnocrotaphus Jan bruin 1 1.61 0 1 0.30 curvidens Monodactylus Mooney 0.00 1 0.36 1 0.30 falciformis Mugil cephalus Flathead mullet 0 1 0.36 1 0.30 Neoscorpis lithophilus Stone bream 0 1 0.36 1 0.30 Rhabdosargus White stumpnose 0 1 0.36 1 0.30 globiceps Total 62 100 274 100 336 100

58 Recorded catch showed that more fish were caught in Groenvlei (n = 274) than Buffalo Bay (n = 62). Blacktail was caught in the highest numbers, especially in the Groenvlei section (n = 193 for Groenvlei, n = 30 for Buffalo Bay). Other species that made high numerical contributions were galjoen (n = 38); Cape stumpnose (n = 11); belman (n=11); strepie (n =9); and elf (n = 8). These six species made up 88 % (n = 336) of retained fish.

Certain species were caught in only one section. Kob, spotted grunter, sand steenbras, spotted gully shark, banded galjoen, zebra, mooney, flathead mullet, stone bream and white stumpnose were documented only in the Groenvlei section. Bronze bream, musselcracker and Jan bruin were documented only in Buffalo Bay. Using the full data set of 361 fish of 27 species (i.e. fish retained, tagged and released), the Shannon-Weiner diversity index (DI) was calculated per area. Fish documented during the first two months of the pilot study before the study area was split into two sections in October 2008 were excluded. The DI for Buffalo Bay was 0.81 and for Groenvlei was 0.57. The 27 species represented 14 families of which Sparidae made the largest contribution (12 species).

5.1.1 Comparison of catch composition between three data sets

The De Hoop data set had 54 species recorded over the full study period compared with the 35 species recorded from the NMLS data set for Goukamma, and the 27 species from the present study (WWF data set). Figure 14 shows that the De Hoop samples form a tight group with little variance, whereas the Goukamma samples are more scattered. The years 1994 and 1997-2000 in Goukamma group together more tightly.

The years 1993, 2002 and 2003 are outliers in the group. The Goukamma WWF sample is not an outlier but it is on the edge, so this indicates that catch composition has not changed significantly between the study years. The Goukamma samples are more scattered similarity between years in Goukamma was 67.5 % as opposed to 84 % similarity in catch composition in De Hoop. The Goukamma centre is different from De Hoop but this was expected as these are two different areas.

59

Figure 14. A multi-dimensional scaling (MDS) plot of catch composition at Goukamma and De Hoop MPAs for several years. The three data sets all used different monitoring techniques. Goukamma WWF (G2009) refers to the present study.

60

Figure 15. Cluster analysis of the same data sets used in Figure 14. G2009 (red) refers to the present study, and green indicates the De Hoop data set.

In Figure 15 a cluster analysis shows species documented from the three different monitoring methods over the three areas. This analysis shows similarities between years and sites. The De Hoop and Goukamma sites clearly groups together, with 83% similarity among De Hoop samples and 75% similarity among Goukamma samples, ignoring the two outliers. The Goukamma samples are clearly more variable.

Simper results of average similarity on catch composition at Goukamma for the years 1994-2002 showed that catch composition in Goukamma was found to be variable and the overall similarity was 68%. The similarity in species composition among the years is defined by the predominance of blacktail, galjoen, musselcracker and dusky kob (Argyrosomus japonicus). The degree of similarity between the samples suggests that there is no substantial difference in species composition. It was not possible to run a Simper output on the Goukamma WWF data as it only stretched over one year and thus could not be used alone to determine if catch composition has changed.

Similarity in catch composition in De Hoop during 2000-2010 was high (84 %) and catch composition was stable between years. This resulted in the MDS plot grouping

61 the De Hoop samples tightly. The similarity in species composition among the years is defined by the predominance of galjoen, blacktail, lesser guitar fish (Rhinobatos annulatus), elf, white sea-catfish (Galeichthys feliceps) and dusky kob.

Table 10. Results of the mean Bray Curtis dissimilarity of catch composition between the De Hoop data set and the Goukamma WWF 2009 data set.

Group 1GW=Goukamma WWF 1GW 2DH 2DH=De Hoop Scientific name Common name Av. Av. Av. Diss/S Contr Cum. Abund Abund Diss D ib. % % Galeichthys feliceps White sea-catfish 0 1.47 2.56 16.90 6.41 6.41 Pomadasys commersonnii Grunter 1.24 0.04 2.08 9.03 5.20 11.61 Sarpa salpa Strepie 1.24 0.14 1.91 4.65 4.77 16.38 Mustelus mustelus Smoothound 0 1.04 1.81 8.70 4.51 20.89 shyshark Haploblepharus edwardsii Puffadder shyshark 0 1.00 1.73 7.33 4.33 25.23 Lithognathus mormyrus Sand steenbras 1.01 0.13 1.54 3.77 3.85 29.08 Poroderma africanum Striped catshark 0 0.86 1.49 4.25 3.73 32.81 Rhabdosargus globiceps White stumpnose 0.85 0 1.48 29.14 3.71 36.51 Carcharhinus brachyurus Copper shark 0 0.80 1.37 1.95 3.43 39.94 Poroderma pantherinum Leopard catshark 0 0.77 1.33 6.88 3.32 43.26 Monodactylus falciformis Mooney 0.72 0 1.25 29.14 3.12 46.38 Eugomphodus taurus Sand tiger shark 0.85 0.14 1.25 2.94 3.12 49.50 Mugil cephalus Flathead mullet 0.72 0.04 1.17 5.14 2.92 52.42 Dichistius capensis Galjoen 1.84 2.49 1.14 4.83 2.86 55.27 Diplodus sargus Blacktail 2.82 2.16 1.14 7.69 2.85 58.12 Epinephelus guaza Yellow belly 1.01 0.37 1.11 1.98 2.78 60.90 rockcod Myliobatis aquila Common eagle ray 0 0.59 1.03 2.68 2.57 63.47 Lithognathus lithognathus White steenbras 0.72 1.27 0.96 8.34 2.40 65.88 Rhabdosargus holubi Cape stumpnose 1.31 0.75 0.96 6.35 2.40 68.27 Neoscorpis lithophilus Stone bream 0.72 0.23 0.85 2.05 2.12 70.39 Diplodus cervinus Zebra 0.72 1.18 0.81 5.90 2.02 72.41 Clinidae Klipfish 0 0.46 0.79 1.75 1.96 74.37 Boopsoidea inornata Fransmadam 0 0.44 0.76 1.79 1.89 76.26 Dasyatis pastinaca Common stingray 0 0.41 0.69 1.42 1.73 77.99 Galeorhinus galeus Tope shark 0 0.38 0.66 1.36 1.65 79.64 Dichistius multifasciatus Banded galjoen 0.72 0.34 0.65 1.29 1.64 81.28 Pomatomus saltatrix Elf 1.24 1.57 0.60 1.89 1.51 82.78 Argyrosomus japonicus Kob 1.12 1.46 0.58 2.37 1.45 84.24 Gymnura natalensis Butterfly ray 0 0.32 0.54 1.15 1.36 85.60 Haploblepharus fuscus Brown shyshark 0 0.30 0.53 0.88 1.32 86.91 Pachymetopon grande Bronze sream 0.85 0.57 0.50 1.20 1.25 88.16 Chrysoblephus laticeps Red roman 0.94 0.66 0.49 2.12 1.23 89.39 Rhinobatos annulatus Lesser guitar fish 1.27 1.56 0.49 2.35 1.23 90.62

Table 10 shows which species caused the most difference in the MDS plot. Species causing the most differences are ranked first. Differences between the De Hoop and Goukamma sites were caused by a higher abundance of white sea-catfish, grunter 62 (Pomadasys commersonnii), Strepie (Sarpa salpa), smoothound shyshark (Mustelus mustelus) and puffadder shyshark (Haploblepharus edwardsii).

63 5.2 Size distribution of retained fish

The fork lengths obtained for the numerically important species in the surveys were converted to total lengths using the equations provided by Mann (2000). Table 11 summarizes the size data for all fish documented during the present study. Size distributions of these species were compared to those from the fishery-independent data from the De Hoop MPA.

Table 11. Sizes of all documented fish caught in the Goukamma MPA from October 2008 – December 2009. Size in mm Scientific name Common name Total Minimum Maximum Average No. Diplodus sargus capensis Blacktail 238 60 400 320 Dichistius capensis Galjoen 43 290 538 392 Umbrina robinsonii Belman 11 296 540 436 Rhabdosargus holubi Cape stumpnose 11 210 360 279 Rhinobatos annulatus Lesser guitar fish 10 780 1045 879 Pomatomus saltatrix Elf 9 350 510 400 Sarpa salpa Strepie 9 150 210 173 Pomadasys commersonnii Spotted grunter 6 295 648 403 Argyrosomus japonicus Kob 6 560 800 690 Lithognathus mormyrus Sand steenbras 4 180 235 200 Epinephelus marginatus Yellow belly rockcod 4 275 390 313 Sparodon durbanensis Musselcracker 3 500 650 590 Chrysoblephus laticeps Red roman 3 315 470 370 Triakis megalopterus Spotted gully shark 3 670 835 768 Pachymetopon grande Bronze bream 2 400 440 420 Charcarias taurus Raggedtooth shark 2 1300 1800 1550 Rhabdosargus globiceps White stumpnose 2 250 330 290 Dichistius multifasciatus Banded galjoen 1 380 Amblyrhyncotes honckenii Blaasop 1 200 Mugil cephalus Flathead mullet 1 140 Gymnocrotaphus Jan bruin curvidens 1 330 Monodactylus falciformis Mooney 1 225 Cymatoceps nasutus Poenskop 1 385 Squalus megalops Shortnosed spiny dogfish 1 592 Neoscorpis lithophilus Stone bream 1 330 Lithognathus lithognathus White steenbras 1 545 Diplodus cervinus Zebra hottentotus 1 318 Total 361

64 From a total of 361 fish, 336 fish were retained, 20 were released and five fish were tagged. Minimum, maximum and average sizes are shown in Table 12 with the most numerically important species first. During October 2008 – August 2009, 27 species were documented. The six numerically important species made up 331 individuals of the total catch.

Blacktail had the highest numerical contribution and 238 individuals were measured. Sizes ranged between 60 mm and 400 mm with an average length of 320 mm (stdev = 80). A total of 43 galjoen was landed and its size ranged between 290 mm and 538 mm with an average size of 392 mm (stdev = 60.8). Cape stumpnose contributed to 11 individuals of landed catch and ranged between 210 mm and 360 mm with and average size was 279 mm (stdev = 50). The undesirable lesser guitar fish did make a notable numerical contribution and interestingly all these fish were caught, measured and released in the presence of the interviewer (pers. obs.). From the 10 fish landed measurements were taken and its size ranged between 780 mm and 1045 mm with an average size of 879 mm. Elf contributed to nine individuals and its size ranged between 350 mm and 510 mm with an average size of 400 mm (stdev = 72.4). Strepie contributed to nine individuals and its size ranged between 150 mm and 173 mm with an average size of 210 mm (stdev = 22.8). The remaining 20 species contributed to six or less individuals and 10 species were only documented once during the study.

Of the six numerically important species that were kept, six undersize fish were recorded. This contributed to 2.2% of the total retained catch. Belman had the highest percentage of undersize fish retained (18% by number), followed by galjoen (2.4 %) and blacktail (1.6%).

65 5.3 Catch per unit effort Six species contributed numerically which could be used to calculate a reliable CPUE baseline. The remaining 21 species numerical contributions were deemed insufficient to calculate a reliable CPUE baseline. Species-specific CPUE is reported per month in Table 12. CPUE fluctuated per species, over areas and months. The autumn and winter periods in general had higher catch rates than the warmer months especially in the Groenvlei section. No catch was documented in February and September for Buffalo Bay whereas Groenvlei had catch documented over all the months sampled. Blacktail had the highest CPUE in both sections throughout the year.

Table 12. Catch per unit effort of six numerically important species in the two sections of the Goukamma MPA. Groenvlei Month Blacktail Galjoen Cape stumpnose Belman Strepie Elf January 0.094 0 0.007 0.006 0.013 0 February 0.086 0.012 0 0 0 0 March 0.111 0.102 0.029 0.014 0 0.01 April 0.053 0 0.004 0 0 0.005 May 0.119 0.025 0 0.003 0 0.007 June 0.151 0.021 0 0 0 0 July 0.079 0.053 0 0.009 0 0 August 0.248 0.071 0 0.003 0 0 September 0.234 0.063 0 0.13 0 0 October 0.172 0 0 0 0 0.021 November 0.089 0.013 0 0 0 0 December 0.155 0 0 0 0 0 Average 0.133 0.030 0.003 0.014 0.001 0.004 Buffalo Bay Month Blacktail Galjoen Cape stumpnose Belman Strepie Elf January 0.033 0 0.033 0.013 0.028 0 February 0 0 0 0 0 0 March 0.076 0.076 0 0 0.151 0.008 April 0.069 0 0 0 0.185 0 May 0.07 0 0 0 0 0.006 June 0.199 0 0 0 0 0.023 July 0.167 0 0 0 0 0 August 0.051 0.008 0 0 0 0 September 0 0 0 0 0 0 October 0.059 0.009 0.042 0 0.069 0 November 0 0 0.059 0.037 0 0 December 0 0 0 0 0.04 0 Average 0.060 0.008 0.011 0.004 0.039 0.003

The ranking of CPUE per species was different for the two sections. In Groenvlei blacktail has the highest CPUE, followed by galjoen, belman, elf, Cape stumpnose 66 and strepie. Blacktail also had the highest CPUE in Buffalo Bay, but were followed by strepie, Cape stumpnose, galjoen, belman and elf. Combined CPUE values for both sections of the Goukamma MPA showed that blacktail had the highest value (0.193 fish per hour), followed by galjoen (0.038 fish per hour), belman (0.018 fish per hour), Cape stumpnose (0.014 fish per hour), strepie (0.040 fish per hour) and finally elf (0.007 fish per hour). The overall CPUE of the six numerically important species based on annual catch estimates and total estimated effort was 0.18 fish per hour.

67 5.3.1 Size composition of individual species Results on the numerically important species are given in terms of contribution to catch, length frequencies and CPUE. blacktail, galjoen, belman and elf, caught at De Hoop are also shown.

(i) Blacktail

120 n = 238 100

80 r e b 60 m u

N 40

20

0

0 0 0 0 0 0 0 0 0 0 5 0 5 0 5 0 -1 -2 -2 -3 -3 -4 -4 -5 0 0 0 0 0 0 0 0 5 5 0 5 0 5 0 5 1 2 2 3 3 4 4 Total length (mm)

Figure 16. Size distribution of blacktail caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size.

0.1 0.09 n = 18613 0.08

y c

n 0.07 e u 0.06 q e r

f 0.05

e v

i 0.04

t a l 0.03 e

R 0.02

0.01 0 0 100 200 300 400 500 600

Total length (mm)

Figure 17. Size distribution of blacktail caught during surveys at the De Hoop MPA from 1984-2010. 68

Blacktail dominated the catch in both sections of the reserve, contributing 66 % of the total catch (Table 9). The mean size of blacktail landed in the MPA was 320 mm (TL) (Figure 16) and were higher than the 297 mm mean size caught at De Hoop (Figure 17).

The coefficient of variation in size over the 12 months was 0.25 % for the MPA. Catch rates show an annual CPUE of 0.13 fish per hour in Groenvlei and 0.06 fish per hour in Buffalo Bay (Table 12). Average CPUE for the MPA is 0.096 (fish per hour) (stdev = 0.07). The coefficient of variation in CPUE over the 12 months was 0.76 % for the MPA. Highest monthly CPUE was documented in the spring months of August and September in the Groenvlei section at 0.25 and 0.23 fish per hour respectively. T-test results show that there is a highly significant difference in CPUE between the two sections (P < 0.00). The variance of CPUE between months was low and both sections had a variance of 0.0042. The CV of the CPUE varies more than the CV of the size of this species.

69 (ii) Galjoen

Galjoen contributed 11% of total catch of which 92% was caught in the Groenvlei section (Table 9). The mean size of galjoen in the Goukamma MPA was 392 mm TL (Figure 18), ranging between 290 mm and 538 mm. The coefficient of variation in size was 0.14 % for the MPA. The mean size (Figure 19) of galjoen in De Hoop was lower (359 mm).

Annual CPUE of galjoen in the Buffalo Bay and Groenvlei sections was 0.008 and 0.030 fish per hour respectively (Table 12) with an average of 0.01 fish per hour (stdev 0.03) for the Goukamma MPA. Variation in CPUE within the two sections was relatively low with a variance of 0.052 in the Groenvlei section and 0.0042 in Buffalo Bay. The coefficient of variation in CPUE over the 12 months was 1.5 % for the MPA. The T-test showed that there was a significant difference in CPUE between the two sections (P <0.05). The CV of the CPUE varies more than the CV of the size of this species.

16 n = 41 14

12

r 10 e b 8 m u

N 6

4

2

0

0 0 0 0 0 0 5 0 5 0 5 0 -3 -4 -4 -5 -5 -6 0 0 0 0 0 0 0 5 0 5 0 5 3 3 4 4 5 5 Total length (mm)

Figure 18. Size distribution of galjoen caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size.

70 0.08

0.07 n = 32594

y 0.06 c n

e 0.05 u q e r

f 0.04

e v i

t 0.03 a l e

R 0.02

0.01

0 0 100 200 300 400 500 600 700 Total length (mm)

Figure 19. Size distribution of galjoen caught during surveys at the De Hoop MPA from 1984-2010.

71 (iii) Strepie

5 n = 9

4

r 3 e b m u

N 2

1

0 170-190 190-210 210-230 240-260 Total length (mm)

Figure 20. Size distribution of strepie caught in the Goukamma MPA from August 2008 – December 2009.

Strepie contributed 2.68% of the catch and most were caught within the Buffalo Bay section (Table 9). The average size was 201 mm TL with a size range of 175-244 mm (Figure 20). The CV in size was 0.11 % for the MPA.

CPUE was highest during March and April (Table 12) and the species were caught only during the warmer months. CPUE was low for the Groenvlei section with an annual CPUE of 0.0009 fish per hour. CPUE in the Buffalo Bay section was 0.039 fish per hour (variance 0.0582). Annual CPUE for the MPA is 0.02 fish per hour (stdev = 0.04) with a significant difference in the CPUE between the two sections (P < 0.05). The CV in CPUE was 1.67 % for the MPA over the 12 months. A greater variation was found in the CPUE CV than the size CV for this specie.

72 (iv) Cape stumpnose

Cape stumpnose contributed 3.27% to the total catch (Table 9). Mean size of Cape stumpnose was 280 mm TL and its size ranged between 221 mm and 356 mm (Figure 21). The CV in size was 0.18 % for the MPA.

CPUE for Buffalo Bay was 0.011 fish per hour and 0.003 fish per hour for Groenvlei (Table 12) with an average CPUE of 0.07 (fish per hour) (stdev = 0.01) for the MPA. Variance in CPUE was low (0.0012 and 0.0092 for the two sites respectively). The coefficient of variation in CPUE was 2.21 % for the MPA. There was no difference found in the CPUE between the two sections (P > 0.05). A greater variation was found in the CPUE CV than the size CV for this specie.

5 n = 11 4 r

e 3 b m u 2 N

1

0 200-250 250-300 300-350 350-400 Total length (mm)

Figure 21. Size distribution of Cape stumpnose caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size.

73 (v) Belman

Belman and Cape stumpnose contributed equally to the catch with 3.27% (Table 9). Average size of belman landed was 435 mm TL (Figure 22).

5 n = 11

4

r 3 e

b m

u

N 2

1

0 250-300 300-350 400-450 450-500 500-550

Total le ngth (mm) Figure 22. Size distribution of belman caught in the Goukamma MPA from August 2008 – December 2009. Dotted line indicates minimum legal size.

0.07

0.06 n = 1409

y

c 0.05

n e

u

q 0.04

e r f

e 0.03 v

i t a

l e 0.02 R

0.01

0 100 200 300 400 500 600 700 800 900

Total length (mm)

Figure 23. Size distribution of belman caught during surveys at De Hoop MPA from 1984-2010.

74 In De Hoop, the average size of belman was 442 mm TL (Figure 23). The CV in size for the MPA over the 12 months was 0.18 %.

CPUE for Buffalo Bay and Groenvlei was 0.004 and 0.014 fish per hour (Table 12), with a variance of 0.0422 and 0.0022 respectively. Overall average CPUE was 0.009 (fish per hour) (stdev = 0.02) for the MPA. Variance between the CPUE of Groenvlei and Buffalo Bay was 0.00132 and 0.00012 respectively. The CPUE CV was 2.21 % for the MPA. No significant difference was found in the CPUE between the two sections (P > 0.05). Undersized fish (18.2%) were documented for this species , anglers would often respond report they did not know the legal size of belman.

75 (vi) Elf

Elf contributed 2.38% to total catch (Table 9). The size distribution in Figure 24 was bimodal, with an average of 400 mm TL (range 350 mm – 510 mm TL). The coefficient of variation in size was 0.16 % for the MPA. Modal size shifts are apparent in De Hoop but average size of this species is 552 mm (Figure 25).

5 n = 9

4

r 3 e b m u

N 2

1

0 350-400 400-450 450-500 550-600 Total length (mm)

Figure 24. Size distribution of elf caught in the Goukamma MPA from August 2008 – December 2009.

0.05

y 0.04 c n e u

q 0.03 e r f

e

v 0.02 i t a l e

R 0.01

0 200 300 400 500 600 700 800 900 1000 Total length (mm)

Figure 25. Size distribution of elf caught during surveys at De Hoop MPA from 1984- 2010. 76 CPUE was 0.003 (fish per hour) for Groenvlei and 0.004 (fish per hour) for Buffalo Bay (Table 12). No significant difference was found in the CPUE between the two sections (P > 0.05) and average CPUE for the MPA was 0.003 (fish per hour) (stdev = 0.006). Variance in CPUE was high for both sections with a variance of 4.112 for Groenvlei and 4.542 for Buffalo Bay. The CPUE CV was 1.97 % for the MPA. A greater variation was found in the CPUE CV than the size CV for this specie.

77 5.4 Catch Estimates Total annual catch of the six numerically important species is shown in Table 13. An estimated 3897 fish were landed of the top six numerically important species in Goukamma during 2009. All numbers of fish caught are an estimation based on the equations and not actual catches.

Table 13. Estimated numbers of fish caught of the six numerically important species for the year 2009. Groenvlei Cape Month Blacktail Galjoen stumpnose Belman Strepie Elf Total January 104 0 8 7 19 0 138 February 77 10 0 0 0 0 87 March 194 178 51 25 0 18 466 April 57 0 5 0 0 6 67 May 168 35 0 4 0 10 218 June 176 24 0 0 0 0 201 July 147 98 0 17 0 0 262 August 318 91 0 4 0 0 412 September 236 63 0 131 0 0 430 October 56 0 0 0 0 7 62 November 29 4 0 0 0 0 33 December 105 0 0 0 0 0 105 Subtotal 1665 505 63 188 19 41 2481 Buffalo Bay Cape Month Blacktail Galjoen stumpnose Belman Strepie Elf Total January 48 0 48 19 31 0 146 February 0 0 0 0 0 0 0 March 33 33 0 0 264 3 333 April 17 0 0 0 198 0 216 May 40 0 0 0 0 3 43 June 163 0 0 0 0 19 182 July 327 0 0 0 0 0 327 August 31 5 0 0 0 0 36 September 0 0 0 0 0 0 0 October 29 5 20 0 22 0 76 November 0 0 18 11 0 0 30 December 0 0 0 0 27 0 27 Subtotal 688 43 87 31 542 25 1416 MPA Total 2353 548 150 219 561 66 3897

Groenvlei had an estimated 2481 fish landed compared with Buffalo Bay’s estimated 1416 fish. The cooler months had the highest catches. March (n = 466), August (n = 412) and September (n = 430) had the highest estimated number of fish landed in

78 Groenvlei. March (n = 333), April (n = 216) and July (n = 327) were the numerically important months for catches in Buffalo Bay. Blacktail were the most important catch in number over both sections and were caught throughout the year in Groenvlei (n = 1665) and mostly in the cooler months at Buffalo Bay (n = 688). August had the highest number of blacktail (n = 318) landed in Groenvlei and this is the highest number for any species caught in a month within the MPA. Galjoen were caught second most in Groenvlei (n = 505) and ranked third in Buffalo Bay. Catch were documented throughout the galjoen season apart from Apri., Out of season catches were taking place during February and November. Belman ranked third in Groenvlei (n = 188) and fourth in Buffalo Bay (n = 31). It was caught sporadically over the year with a peak in September (n = 131), and only during the summer months in Buffalo Bay. Cape stumpnose ranked fourth (n = 63) in Groenvlei and third in Buffalo Bay (n = 87) and were caught almost exclusively during the warmer months. Elf were fifth (n = 41) in Groenvlei and sixth (n = 25) in Buffalo Bay and were caught between March and October. Strepie ranked last (n = 19) in Groenvlei but second most (n = 542) in Buffalo Bay and no catches were estimated to be landed in the winter months. A T- test between the catch estimates showed that there was no significant difference between Groenvlei and Buffalo Bay (P = 0.06). Most fish were caught in Buffalo Bay during the first six months with no catches estimated in February and September. Although fish were landed throughout the year at Groenvlei, the winter period had the highest number of fish caught.

79 5.5 Discussion

Diversity differed between the two sections, with Buffalo Bay scoring higher than the Groenvlei section, although more fish were caught in Groenvlei. Habitat difference can explain higher diversity. In Buffalo Bay a more diverse habitat is found consisting of five of the seven habitats found in Goukamma. The inter-tidal and sub tidal reefs are found predominantly in the Buffalo Bay section (Figure 3). The Groenvlei section is much larger but more homogenous, and consists of only three of the seven habitats found in the MPA.

Regarding catch composition, the MDS plots and cluster analysis show that the Goukamma WWF data set groups with the Goukamma NMLS data set in similarity, thus I conclude that catch composition has not changed significantly between the two study periods at Goukamma.

Dissimilarity in catch composition between De Hoop and Goukamma could be ascribed to the different fishing methods, biogeography, fish abundances or the effects of exploitation. The main reason for the differences between the Goukamma and De Hoop data is probably the method of fishing (fishery-dependant at Goukamma vs. fishery-independent at De Hoop). Anglers in Goukamma use variable fishing methods and have different levels of skill. The controlled fishing in De Hoop is research-based and always follows the same methods and uses mostly the same anglers with similar fishing skills (C. Attwood pers. comm.). Fish populations are more abundant in De Hoop than Goukamma. The De Hoop populations have increased following closure of the MPA in 1985 to recreational fishing. Consequently, sample sizes are greater in De Hoop, resulting in lower variability in catch statistics (Bennett and Attwood 1991, Bennett and Attwood 1993). The higher abundance gives rise to stable populations and consequently inter-annual variation in catches is lower (C. Attwood, pers. comm.). The data used were restricted to the time after initial stock recovery. The benefit of MPAs on fish stock recovery and protection has been shown in several publications (Attwood and Farquhar 1999, Gell and Roberts. 2003). Higher abundance partly explains the lower variability in the data but generally, diversity decreases from east to west, making the explanation that the difference is caused by biogeography less likely (Turpie et al. 2000).

80 Species causing differences in the MDS plot between Goukamma and De Hoop are discussed below. A higher abundance of the white sea-catfish is documented in De Hoop, but no catch was documented in Goukamma in 2009. This can partly be explained by the fact that anglers view these as undesirable fish owing to their venomous spines and are thus more likely to discard them (Heemstra and Heemstra 2004). The spotted grunter was found in higher abundance in Goukamma than in De Hoop. Biogeography and the fish’s ecology could be responsible for this. Grunter are common in estuaries and is often caught near river mouths. The two areas within De Hoop that were sampled (Lekkerwater and Koppie Alleen) are not in close proximity to a river. Strepie, also caused large differences because they are caught in Goukamma with a smaller hook size than used in the De Hoop study. These fish are caught for bait. Smoothound and puffadder shyshark are recorded in the De Hoop study but not in the Goukamma study as sharks are not seen as ‘edible’ fish. In Goukamma, anglers are more likely to release these fish than keep them, whereas all fish in De Hoop that are caught are recorded.

Blacktail and galjoen had the highest CPUE in the MPA and this was significantly higher in the Groenvlei section. Blacktail had highest catch rates throughout the year, but peaked during winter months. This makes blacktail the most important species in terms of numbers. The species is a good example of sequential target switching as historically it was never a prime target species and today it is the third most important shore angling species in South Africa (Attwood and Farquhar 1999, Mann 2000). Blacktail is a long-lived species (21 years), becomes resident following recruitment and adults inhabit the same habitat as galjoen (i.e. shallow, rocky and sandy substrata) (Attwood and Farquhar 1999, Mann 2000). Average size of blacktail is bigger in Goukamma than De Hoop, but CPUE is much lower in Goukamma. CPUE at De Hoop showed inter annual variation, with a peak in CPUE (1 fish per hour) during 1992-1998 following closure to recreational fishing due to a recruitment phase. Recruitment size in De Hoop was found to be between 225 mm and 274 mm. Fewer individuals (n = 54) were measured between the 200-300 mm class range which is the recruitment stage. The fact that there are larger average sized fish found in Goukamma than De Hoop is of concern as this can indicate that recruitment is not taking place. In the Pradervand and Hiseman (2006) study, monthly CPUE was highest in summer months and blacktail contributed only 39% of total catch, although the shift in catch proportions (i.e. 39 % vs. 66 %) is another 81 cause for concern, given their vulnerability to fishing. It can also be argued that smaller fish are put back in Goukamma, or that smaller hook sizes are used in De Hoop, resulting in a false under-representation of small fish in Goukamma. With more catch data collected over time, growth and mortality rates can be estimated and could provide productivity estimates to guide future fisheries management (Casselman 1987).

Goukamma is a favored fishing area for galjoen owing to the mixed shore habitat found there (Attwood 2003). This species is also actively targeted by the angling community in general (Brouwer et al. 1997), even outside of its allowable fishing season (pers. obs.). A decline in this stock was noticed during the 1930s (Smith 1935) and the species was deemed as collapsed, based on per recruit analysis (Griffiths 1997a). Galjoen is also known to be resident and is therefore vulnerable to over-exploitation and recovery is slow (Attwood and Cowley 2005). The galjoen population were only recovered ten years after De Hoop was closed to fishing in 1985, thus we assume that the De Hoop CPUE reflects recovered populations. Mean size of galjoen was smaller in De Hoop than in Goukamma, but the CPUE is higher (0.5 fish per hour) in De Hoop than the annual CPUE of 0.001 fish per hour in Groenvlei. CPUE and size data could indicate that fish are being caught at a faster rate than that at which the stock can replace itself, when compared to De Hoop. Undersize fish made up 2.4% of total measured catch. Catch was also documented outside the legal fishing season (i.e. February and November). When compared with the Pradervand and Hiseman (2006) study, galjoen was found to have a larger contribution (39%) in number over the ten year period than in the 2009 study (11%).

CPUE values for Cape stumpnose were highly seasonal because the adults migrate in spring towards the Eastern Cape waters of South Africa and are found in mixed shore habitat (Mann 2000, Heemstra and Heemstra 2004). These species are estuarine dependant and habitat degradation of estuarine breeding areas has been attributed to reduction in biomass of these fish. Although Cape stumpnose was documented in the Pradervand and Hiseman (2006) study, there was insufficient data to calculate CPUE. There was no size or CPUE data for Cape stumpnose available from the De Hoop study.

82 Belman is a shallow-water species with a wide distribution (from False Bay to Madagascar and Oman). Adults are normally found from the surf-zone to 40m depth, over sub tidal reefs where they have been observed congregating in shoals in caves, and are more active at night (Mann and Cowley 2000, Hutchings and Griffith 2005). Belman are not often caught by recreational anglers from the shore or by boat anglers due to the above mentioned reasons (Mann and Cowley 2000). De Hoop shows significant inter-annual change in Belman CPUE, which varies greatly between the two sites: Koppie Alleen (> 0.05 fish per hour) and Lekkerwater (<0.05 fish per hour), with annual catches showing an inter-annual variation of 25% of catch since closure. Interesting to note is that belman was found to have inter-annual fluctuations between Groenvlei and Buffalo Bay, where it was only documented in Buffalo Bay during the summer months and mostly during winter in Groenvlei. Belman was reported in the Pradervand and Hiseman (2006) study as having a numerically significant contribution to catch, 70 individuals were documented over the ten year period.

Strepie is the second most important angling species (in number) on the east coast of South Africa with highest catch rates during summer (Clarke and Buxton 1989). High summer catch rates partly explain the high variance in CPUE. The strepie contributed to 8.6% of total catch in the Pradervand and Hiseman (2006) study. These species favor rocky areas and a higher variance can be partly explained by their schooling behavior (Heemstra and Heemstra 2004).

Elf is the most important recreational shore angling species on the southern and eastern coast of South Africa and makes up the highest number in angling catch (Govender and Radebe 2000). Monthly CPUE data from the Pradervand and Hiseman (2006) study shows a similar distribution, where elf was predominantly caught in the first four months of the year. This is explained by the fact that they are in Cape waters during these months and migrate towards Kwa-Zulu Natal in winter. The De Hoop data showed that the modal size differed between the months of February, May, July and November. Interestingly, although the sample size of Elf is very small (n = 9), it mimics the bimodal size distribution at De Hoop (Figures 23 and 24). Elf was monitored at De Hoop from 1994 and initial CPUE was 0.01 fish per hour and has remained at a relatively stable value of 1 fish per hour since 2005.

83 Overall CPUE for the MPA was 0.018 fish per hour, which is substantially lower than similar studies. For instance, Beckley et al. (2008) found in their study of Richards Bay an overall CPUE of 0.064 fish per hour which they concluded was ‘very low’. Attwood and Farquhar (1999) declared fish stocks to be collapsed between Cape Hangklip and Walker Bay with an average CPUE of 0.32 fish per angler day, however this is still higher than the CPUE at Goukamma when converted to fish per hour. The numerical contribution of blacktail and galjoen are significantly more, which can mask the overall CPUE for Goukamma. It remains difficult to compare these studies done to the present study as habitats, fish communities; protection status and biogeography are different. Both Attwood and Farquhar (1999) and Beckley et al. (2008) used a RCS, but differed in number of surveys done and the times of sampling, comparison is thus only a guideline.

The RCS method used in this study was effective in giving estimates of catch composition, CPUE and catch estimates, for six species in Goukamma. It is however a slow and time consuming process as surveys were mostly done on foot. Safety is a great concern as weather can rapidly change and difficult terrain can make an interviewer vulnerable to a variety of accidents. If these arise interviewers should be equipped and trained to deal with these. Owing to the linear layout of the reserve most of the budget was spent to cover mileage which makes the practicality of such monitoring, costly (Appendix 4).

Despite these disadvantages, we were able to document a trend in catch composition and seasonal catch rates which corresponds with other literature. All species which made important numeric contributions inhabit rocky and mixed shores, or are estuarine dependant which correlates with the habitats found within the reserve. In Chapter four mixed and rocky shores were found to be fished significantly more in density per km of habitat.

84 Chapter 6 Conclusions and recommendations

Collapse of line fish species have been attributed to the response of fish stocks complex life cycles and ecology (i.e. being hermaphroditic, long lived and being largely resident in small areas) to fishing pressure. These ecological characteristics make them vulnerable to over fishing. In South Africa, of the 250 linefish species which have been documented only a few have been researched properly with life cycles being described adequately. The problem lies in that the recreational fishery is multi-species’ based; few individuals are caught, it has a high catch collectively, and a low monetary value when compared to most commercial fisheries..

Angling is practiced sporadically and in low densities. Monitoring of recreational catch is therefore difficult in terms of the number of staff required to collect catch data and the cost associated to cover the 3 000 km coastline that is utilized. This has lead to the recreational angling sector being largely unchecked and monitoring of catch directed to a few commercially viable species. Yet it is the most participated marine sport in South Africa and needs to be monitored.

Monitoring programs which were instituted were often found to be of local relevance only and not statistically strong, or comparable between sites. The most comprehensive line fish survey conducted relied heavily on angler reported catch cards. This makes the data questionable as it is open to recall and prestige bias as well as over reporting by more avid anglers who show a greater interest in fishing. Additionally data were gathered opportunistically so annual catch and effort data could not be equated, from which CPUE is derived.

Species specific CPUE is a preferred measure of fish stock abundance which requires large samples of catch data over month, season and years. Length frequencies which indicate annual and seasonal age, and growth cycles are used in association with CPUE data to assess fish stock health. This method also requires large samples of measurement throughout the year to determine an accurate assessment. As there is a lack of knowledge on the abundance of linefish species

85 which existed pre human exploitation, it is difficult to measure the sustainability of exploitation rates against present day data in already exploited areas. Data which is available can often not be statistically compared due to sampling design.

The aforementioned reasons strongly motivate for a uniform monitoring technique to provide vital information on these exploited and collapsed stocks. A sound survey design which gives accurate area specific data on catch and effort over years, months, day types and habitat are necessary. This research focused on testing the RCS method which provides statiscally comparable results and lays the foundation for a long-term monitoring program. Monitoring of fish stocks before and after closure of MPAs through research based angling has shown trends in the recuperation of fish stocks through long-term monitoring programs. By using a uniformed stratified monitoring technique to sample fishing effort in the correct ratio we can compare the state of fish stocks over time with fish stocks from ‘no-take’ MPAs which are similar in habitat

The RCS method tested in this study for Goukamma MPA proved to be a suitable, successful and financially viable option for CapeNature. The method gave us the opportunity to actively seek out anglers in the MPA which have many access sites. It further gave vital spatial information with regards to understanding how much fishing effort is directed to the different habitat types. This could not have been possible through an Access point survey design. The survey design using a 3:1 weekend weekday ratio based on a five month pilot study was found adequate to base the stratified random sampling to determine effort. From the GLM we could statistically show that effort distribution over day types and month varied significantly and that this was a response to fishing seasons, holidays and day types. From the effort data we could estimate annual effort in angler numbers and hours. The catch gathered during this study were enough to form a species specific CPUE series for six species from which annual catch estimates could be derived based on annual effort according to month types. We also showed that there was a significant difference in these species caught and that blacktail is the most commonly caught species in Goukamma. In comparison to De Hoop a lower CPUE were found for blacktail, galjoen, elf and belman than in Goukamma.

86 Spatial distribution of anglers could be significantly related to habitat type and this showed that anglers favored rocky areas to fish from. From the data we could conclude that Groenvlei had the highest effort in hours fished but angler effort in numbers was less due to the fact that access was limited by tide and terrain. Densities for Groenvlei were highest along the first 2 km at the Platbank entrance (west to east). Buffalo Bay’s effort was found to be higher in angler numbers and densities per 100 m section were higher than Groenvlei. We were able to identify that the ‘sand above rock habitat’ in Buffalo Bay were fished significantly more than any other habitat in Goukamma. This habitat was found to be the nearest good fishing habitat in relation to the popular Buffalo Bay beach. Groenvlei had more fish caught but a lower diversity than Buffalo Bay which had less fish caught and a higher density which were related to habitat type. We further found that fish species which were caught most, showed a low CPUE in comparison to the De Hoop MPA. Average length frequency of blacktail is a cause of concern. This species is affected by the current fishing pressure.

The RCS was however not without problems and survey design and implementation was complex. The long walk along the Groenvlei section is dangerous, especially when weather and tide conditions are unfavorable. There is also the risk of a single interviewer encountering aggressive anglers in isolated areas (although no interviewer was ever physically harmed during this study). Perhaps the most challenging factor is the linear nature of the Groenvlei section. Vehicles need to be parked at each end to drop off or pick up the surveyor, so an assistant is required to do this, which requires more funding. Most surveys are also conducted on a weekend which is a time when researchers, employees and students would rather not be working.

If CapeNature chooses to continue with the RCS, mileage costs (currently R25 000 per annum) could be considerably reduced, if deployment and recovery of surveyors could coincide with reserve-related duties. A word of caution however that surveys should be kept separate from the law enforcement duties (conducted by rangers) as anglers are often not willing to disclose illegal catch. Law enforcement is also done opportunistically and favors times of high angling effort, bringing in bias. Third year Nature Conservation students are currently paid R2 500 per month, with free

87 accommodation on the reserve. The student can conduct the surveys and enter the data, which should be analyzed by experienced scientists.

We now have data to stratify sampling according to monthly effort which can also reduce cost and free up time for students. It is also recommended that in future, within-day variability should be monitored. Buffalo Bay surveys could be increased as they are normally completed quickly and anglers rotate fast, especially during the holidays and weekends. Groenvlei takes longer to cover though, and if the logistics and costs of the linear walk become unsustainable, it could be possible to implement an access point survey, because most anglers enter from the Platbank entrance (close to the student accommodation), but spatial data will be lost through such a practice. Thus cost will have to be weighed up against survey design. The benefit of access point surveys is that they gather data on complete fishing trips, thus total catch and effort can be documented.

During the surveys signs of night fishing were observed. Law enforcement by rangers must end this practice, because night fishing reduces the value of effort, catch and CPUE estimates produced by the diurnal surveys.

88 Advantages of the RCS

• The main reason for using the RCS is that there are various access sites in Buffalo Bay leading to fishing areas which could not be covered if an Access survey is done. • The principal advantage of this method within the MPA is that it was conducted on site and anglers are actively sought out • As anglers are actively sought out this gave us the chance to sample areas of low angler activity, such as the Groenvlei section over weekdays. • Spatial data on angler distribution is obtained • It is the strongest method to use in gathering area specific data to estimate catch, effort and CPUE where various access sites to the coast is available. • This method eliminates recall and prestige bias • A greater chance to record species which are viewed as less desirable and often discarded by fishermen is achieved as well as documenting catch and release data. For instance all lesser guitar fish which were recorded during this study were caught and released in front of the interviewers. This greatly helps to estimate change in catch composition over time. • It shares with the Access survey the advantage of catch being examined by trained surveyors thus eliminating incorrect identification of species. • After the first year I had an understanding of seasonal catch and angler distribution over habitat and area. This information can help to further refine survey design by doing sampling according to not only day type effort but also according to monthly effort.

The surveys are a good base from which to conduct environmental education. Although this is not a unique advantage of the RCS, it must be noted that fishermen are known to be un-cooperative and that fishing is a hugely controversial issue which brings about strong emotions and opinions. Fishermen who were encountered often felt that they are a “soft target’ and that we were in essence trying to take away their right to fish by closing the MPA, based on the data that we collected. A lack of understanding regarding the reason of monitoring collapsed fish stocks were often picked up. These issues lead to the “Fishing for the future” angler workshop held at Goukamma. The aim of the

89 workshop was to bring about a greater understanding on the state of recreational fishing, the role of MPAs and the use of the RCS and to answer the fishermen’s’ questions by experts in a relaxed, non-threatening environment. Invitations were send out via post and e-mail from contact details gathered from surveys and invitations were handed out to fishermen during surveys. The workshop was conducted a year into the study and fishermen were comfortable enough to come, as they were familiar with the monitoring team by that time. More than 50 anglers were counted at the workshop and most were either from Sedgefield or Knysna; the workshop was found to be informative. No animosity was picked up and anglers left the workshop with a clear understanding on the fishing problem, the role of MPAs and reasons for the monitoring to be conducted. I thoroughly believe that the camaraderie, respect and friendship which were formed with fishermen over the roughest terrain and weather conditions, while doing the RCS, played a key role in participation of the workshop.

Disadvantages of the RCS • The design of this survey was complex although it is not unique to an RCS. Much time was spent during the five month pilot study to design sampling ratios correctly. The design of the data base was also time consuming. • Estimates of catch rates obtained from mid-trip interviews assume that the rates do not change after the interviews. Incomplete trip data are subject to length of stay bias as anglers who fish for longer are more likely to be encountered than anglers who fish for less. At present there is no way to minimize this bias. • Night time surveys are too dangerous with respect to terrain and conditions, and the risk of encountering aggressive anglers. • As a large sample size is necessary, it can be regarded as a slow method for detecting changes in monitored variables (e.g. average size of fish, and CPUE). • Funding was a major problem initially. Overheads were high as an assistant had to be employed, and a motorbike and laptop had to be purchased. Funding for mileage had to be arranged as private vehicles were also required.

90 Although the Goukamma MPA is open to shore-based angling it should still fulfill its role in managing the marine environment responsibly through sustainable fishing practices. It must protect the quality and quantity of its genetic biodiversity through regular enforcement of daily bag limits, minimum sizes and no night fishing. Monitoring programmes can not decrease fishing pressure or re-build depleted fish stocks, but can provide the data needed to assess the status of the fish resources. Co-operation between management and resource-users is necessary to ensure sustainable fishing practices, while still conserving fish stocks. It is important that anglers fishing within the MPA realize that they are entering a protected area. Thus communication of why legislation is in place and the effects of fishing is paramount so that anglers within the MPA can make educated decisions. Sign boards at entrances should describe the problem of collapsed stocks, and which species the MPA is protecting, with a brief summary of their life cycles (i.e. slow growing, resident). Many anglers encountered were unaware of these issues. It is recommended that a socio-economic study be undertaken to determine the cultural and financial contributions fishing within the Goukamma MPA provides. Contact details of most anglers within the MPA have been collected and can be used for this purpose.

The Goukamma MPA is a popular fishing destination with high angler effort in certain areas. For surf species, it can be viewed as a node of exploitation. Options to regulate or cap effort may be required. Gear restrictions can also be placed on anglers in the reserve (i.e. one rod per person and closure of certain areas. Another option would be to close the MPA to all fishing activity, but it is recommended that this be phased in over a period of time with stakeholder participation. The National Spatial Biodiversity Assessment recommends more ‘no-take’ areas and it would be easier to close this MPA than to declare a new MPA elsewhere. Monitoring data gathered from surveys would then play a role in determining the effects of pre- and post-closure on fish stocks. Post recruitment and recovery of stocks could then be measured by fishery-independent angling, such as currently being done at the De Hoop MPA.

91 Glossary Attitude: Feeling or disposition of a person toward some entity or object. Avidity bias: A source of bias that arises when avid anglers (those who participate more frequently) are disproportionately represented in on-site surveys. Catch: Weight or number of all fish caught, whether the fish are kept or released. Catch per unit effort (CPUE): Weight or number of fish caught per trip, per angler hour, or per some unit of fishing effort. Completed trip interview: Interview conducted as an angler leaves the area after fishing, done in Access surveys. (Compare incomplete trip interview). Contact method: The method used to contact anglers for a survey (mail, door-to-door, roving creel, access point, phone or aerial). Fishing effort (fishing pressure): A measure of resource utilization by anglers which can be measured in angler or angling-party hours. Instantaneous count: Counts of anglers or other activities from a vantage point. Incomplete trip interview: Interview conducted before an angler has finished fishing, done in Roving creel surveys (compare complete trip interview). Length of stay bias: Bias arising from the greater likelihood of sampling anglers who fish for longer periods, than anglers who fish for shorter periods. Prestige bias: Bias arising when surveyed anglers exaggerate the number and/or size of fish caught. Progressive counts: Count of anglers over time as a roving creel surveyor moves through a fishery area (compare Instantaneous count). Recall bias: Bias arising when anglers incorrectly remember past events or the time or place at which they occurred. Response error: Error arising because of recall or prestige bias, or because an angler lied or misinterpreted a question. Roving creel survey: An on-site angler survey during which anglers’ catches are examined by the surveyor. Sampled population: Actual human population from which information is collected. Sampling error: Error arising from improper sample selection such as avidity bias, an incomplete sampling frame or length of stay bias. Sampling frame: Complete set or list of all sampling units (see Sampling unit). Sampling unit: Basic unit of sampling (e.g. an angler or a particular combination of space and time).

92 Stratified sampling: Independent sampling within two or more defined subgroups (i.e. Groenvlei and Buffels Bay) of a sampled population (i.e. the anglers of Goukamma). Survey instrument: The questionnaire or form on which data are recorded during a survey. Three-stage sampling: Form of sub-sampling in which a primary sampling unit (PSU) is chosen first, then a secondary sampling unit (SSU) and a third sampling unit (TSU). In on-site surveys, the PSU is the day, SSU the time and TSU the starting end of the survey.

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