Estimating Fishery Statistics in the Artisanal Fishery of Zanzibar, Tanzania: How Big a Sample Size is Required?

Transcription

1 Western Indian Ocean J. Mar. ARTISANAL Sci. Vol. 1, FISHERIES No. 1, pp. STATISTICS 19 33, 2002 IN ZANZIBAR WIOMSA Estimating Fishery Statistics in the Artisanal Fishery of Zanzibar, Tanzania: How Big a Sample Size is Required? Narriman S. Jiddawi 1, Richard D. Stanley 2 and Allen R. Kronlund 2 1 Institute of Marine Sciences, University of Dar es Salaam, P.O. Box 668, Zanzibar, Tanzania; 2 Pacific Biological Station, Fisheries and Oceans, Canada, Nanaimo, B.C. Canada V9T 6N7 Key words: catch monitoring, artisanal fisheries, coral reefs, Tanzania Abstract We examined the sampling effort required to estimate annual fishery landing statistics from the artisanal fishery. Using the observed variability among landing days in two villages in Zanzibar, Tanzania, we are able to present the relationship between predicted precision and sampling effort and take advantage of the strong correlation between lunar day and landings to demonstrate a potential gain in sampling efficiency of 30 % from stratification. The observed data are then used to simulate a virtual year of landings for the two villages and demonstrate how the simulated year of observations can be used to simulate and assess various sampling strategies. In addition, it provides a heuristic tool for demonstrating the likelihood of how much any one sampled data set may differ from a known value. Finally, we discuss how studies of specific individual landings sites (index sites) could be employed to provide better harvest information for assessing and managing not only the artisanal fisheries of Zanzibar but also smallscale fisheries elsewhere. INTRODUCTION The islands of Pemba and Unguja comprise the state of Zanzibar in the United Republic of Tanzania (Fig. 1a). Artisanal fishing methods in Zanzibar produce an estimated 14,000 t/yr and are reported to provide a source of income to about 14% of the population (FAO, 1994). These fisheries provide the major source of protein for most inhabitants, and virtually the only source of animal protein for low income groups. Although fisheries resources are critical to the prosperity of the Zanzibari people, management of this sub-sector is hampered by the lack of accurate harvest information. As with many smallscale fisheries, current fishery monitoring programmes provide summary data that are sufficient to broadly characterise the fishery, but are not adequate for stock assessment and resource management since they are too imprecise to rigorously characterise trends in landings, let alone other status indicators like catch rate or species composition. While the monitoring process may be well designed in theory, attempting to cover all landings in all landing sites on most days appears overly ambitious given the relative lack of training of the port samplers (Jiddawi & Stanley, 1999a, b). We initiated a study of the daily landings of two villages in northern Unguja Island to examine the practicability of collecting these data, and to demonstrate the value of accurate landings data for assessment and general research. Our principal objectives included: developing statistically defensible estimates of annual landings for both villages; providing baseline information on catch composition, catch rates, and mean weight; demonstrating that we could develop a positive Corresponding author: NSJ jiddawi@zims.udsm.ac.tz

2 20 N.S. JIDDAWI ET AL. working relationship with the fishing community in the two villages. In addition to these primary objectives, we planned to use the study to provide pilot data to guide future survey design for estimating landings. This is the focus of the present work. In this document, we: describe how we estimated annual landings from a biased set of observations; use the variance estimates to characterise the relationship between sample size and precision; use the observations to simulate one year of landings as a gaming tool to investigate different sampling strategies; and discuss the role that index sites could have in the monitoring and research of the artisanal fishery in Zanzibar. MATERIALS AND METHODS Study site Tumbatu Island Mkokotoni Zanzibar 0 20 Km Nungwi Mnemba Island Matemwe INDIAN OCEAN Chwaka Kizimkazi Details of the fisheries in the two village districts are summarised in Jiddawi & Stanley (1999b). Herein, we briefly outline those elements pertinent to the estimation of annual landings. The two study sites were in the village districts of Matemwe and Mkokotoni, located on the northwest and northeast corners of Unguja Island, respectively (Fig. 1). The shoreline of Matemwe, like much of the eastern coast, is dominated by an unbroken fringing reef, km from the shoreline. Mkokotoni landing site is located on the west coast of Unguja Island at the head of a bay. There are patches of coral reefs within the fishing area, but no unbroken fringing or barrier reefs as can be found on the east coast. All participants in the fishery are Muslim so fishing activity is lower on Fridays, the day of prayer. The local fishermen generally land their catches close to home, but sometimes leave their villages to travel and camp on beaches elsewhere on Unguja and Pemba Islands, or as far as the coasts of Mozambique and Kenya. Similarly, a few fishermen from elsewhere occasionally land their catches at Matemwe and Mkokotoni. Zanzibar tides undergo a semi-diurnal tidal change with the succeeding high tides slightly over 12 hours apart and the associated spring/neap tide Fig. 1. Unguja Island along the coast of Tanzania and study sites variation in each fortnight (Tobisson et al., 1998). The tide range during spring tides frequently exceeds 4 m. The fortnightly peak or spring tides occur one or two days after the new or full moon. The days of the large spring tides bracketing the new or full moon are referred to as the bamvua period. These are expressed by fishermen in day counts from the new moon. For example, bamvua covers days 10 to 18, which bracket the full moon on day 14 or 15, and also days and days 1 3, which bracket day 1 of the new moon of the next lunar month. The neap tide days are called the maji mafu period. Fishermen typically report them to be days 4 9 and 19 24, although the day ranges varied slightly among interviews. The timing of the high tides over the lunar cycle is stable such that the peak of the morning high tide on the first day of the new or full moon occurs at about 2 am ± 30 min. throughout the year. Thus the highest morning high tides always occur around 2 3 am. We used tide times and fluctuation for Zanzibar town (6 o 09' S x 39 o 11' E) as the reference station, as defined by UH Sea Level Center,

3 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR 21 University of Hawaii, 1000 Pope Road, MSB 430, Honolulu, Hawaii, Our study focused on the diurnal fishing in both villages. These fisheries account for virtually the entire catch delivered to the auction site. Other market pathways are summarised in Jiddawi & Stanley (1999b). The dominant gear types are those typical of reef and near-reef fisheries: portable traps, small seines, drive-nets, spear without scuba, and handlines (Ruddle, 1996). Octopus are usually collected with a hooked stick by fishermen walking on or near the reef crest; some are caught using spears (Mhitu & Jiddawi 1999). The diurnal fishermen of Matemwe are much more active during bamvua. The fishing day starts near dawn as the fishermen use the early morning high tide to sail over the fringing reef without grounding the vessel. Most fishermen in Matemwe travel to their grounds in crews of 2 4 in doubleoutrigger canoes (ngalawas) that are 5 7 m in length. Travelling time to the grounds varies from hours, depending on winds and fishing ground. Most fishing takes place on or outside the fringing reef or the reef surrounding the nearby atoll of Mnemba (Fig. 1). They fish late during the ebb, the low water slack, and the early part of the rising tide, then return and cross the reef on the rising tide in the afternoon. As the auction site is reached, one or two fishermen disembark with the catch and carry it to the auction near the high water mark. The remainder of the crew continues southwards along the beachfront to anchor the vessel near their homes. In Matemwe, the auction takes place around a one forked stick embedded in the sand, just above the high tide line. Strings of fish are hung on the stick for display as bidding takes place under the supervision of an auctioneer. Mkokotoni fishermen do not have to contend with a fringing reef, thus the fishing activity is more constant within a lunar period. Dhows (daus) are the more common vessel in Mkokotoni. These are double-ended, planked vessels about 6 12 m long with a raked stem, wide beam and low freeboard. They have a deeper draft than the ngalawas of Matemwe as they do not have to go over the reef. Both vessel types are usually powered by lateen sails. Trips which employ the drive-net technique were more common in Mkokotoni and are conducted from the dhows with crews of up to 42 men and boys. Most Mkokotoni trips are day trips only. Fishing grounds are generally less than 4 hours sailing time and are usually less than 2 hours. Fishing grounds for Mkokotoni are more varied. They range from the large sand/mud flats and mangrove forests of the adjacent enclosed bay, to fishing grounds 2 hours sailing west of Tumbatu Island (Fig. 2). The nearest coral reefs to Mkokotoni are km away. The range of depths fished by Mkokotoni fishermen, as reported in interviews, range from the exposed flats and reef crest to over 120 m during trap fishing. The auction process is similar in Mkokotoni but takes place at the seaward end of an open building. The catches are displayed on a concrete table and, as with Matemwe, auctioned in order of arrival. Sampling method Study initiation First, a meeting with representatives of the fishing community in Matemwe and Mkokotoni was set up. The study team explained the purposes of the study, requested permission to sample and asked for guidance on how to conduct the sampling so as to minimise the interference to the unloading and sale of catches. The community granted permission on the condition that the study team did not slow down the auction process. A similar meeting was held at the beginning of the second year to request for continuance of the study. This permission was granted, however, the fishermen asked that the weigh station be moved further from the auction so that the recording of catch weights would not be overheard by potential buyers. It was observed that during the trip home from the fishing grounds, the fishermen took considerable care in stringing the fish on the mtungu, a strong twine of natural fibres. The intent was to pack the mtungu in such a way that the catch volume appeared larger. They did not wish recorded weights to detract from the product presentation. A third meeting was held at the end of the sampling to thank the villagers in each community for their help. Both communities expressed their appreciation of the study for the attention their fishery was receiving from the State government and the University of Dar es Salaam, and requested that it continue.

4 22 N.S. JIDDAWI ET AL. Sampling days Sampling began in mid-march 1995 and continued for two years through March 1997 (Tables 1 and 2). During the first 12 months (Year 1), the choice of sampling days was biased towards days corresponding to spring tides. Fishermen had requested that we only sample during bamvua because these were the best fishing days and therefore most important for us to examine. They also commented that during maji mafu, the auction was often late in the afternoon and they would be in a hurry to sell their fish and return home before dark. We explained that we also needed to see the landings from maji mafu to examine how species composition and catch rates differed within a month, but we allowed the fishermens preference to dictate sampling in Year 1. While sampling was conducted only during the spring tides, sampling days were distributed in an ad hoc manner with respect to season, days of the week, including Fridays, and also included days within the Muslim holy month of Ramadhan. In Year 2, we stratified our sampling by season and within season to include one cycle between Table 1. Sampling days and total landings at the Matemwe auction site from 13 March 1995 to 22 March 1997 (Fridays noted with an *) Year 1 Year 2 AM high AM high Lunar tide height AM high Landings Lunar tide height AM high Landings Date day (cm) tide time (kg) Date day (cm) tide time (kg) 13-Mar : Apr : Mar : May : Apr : Jun : Apr : Jun : Apr : Jun-96* : May : Jun : May : Jun : Jun : Jun : Jun : Jun : Jul :31 1, Jul : Jul : Aug-96* : Aug : Aug : Aug : Sep : Sep : Sep : Sep : Sep : Oct : Sep : Nov : Sep : Nov : Sep :16 1, Dec : Sep : Dec : Oct : Jan :57 1, Oct : Jan : Dec-96* : Feb : Dec : Feb : Dec : Mar :53 1, Dec : Mar : Dec : Mar : Dec :01 1,25 26-Dec :51 1,63 09-Jan : Jan : Mar : Mar : Mar-97* : Mar : Mar : Mar :31 1,022.5

5 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR 23 Table 2. Sampling days and total landings at the Mkokotoni auction site from 15 March 1995 to 12 March 1997 (Fridays noted with an *) Year 1 Year 2 AM high AM high Lunar tide ht. AM high Landings Lunar tide ht. AM high Landings Date day (cm) tide time (kg) Date day (cm) tide time (kg) 15-Mar : Apr-96* : Mar :08 1, May : Apr :08 1, May :14 1, Apr :29 1, Jun : May-95* : Jun : May : Jul : Jun-95* : Jul : Jun :19 1, Jul : Jul-95* : Aug : Jul-95* : Aug-96* : Aug : Aug : Aug : Sep : Sep : Sep-96* : Sep :47 1, Oct : Oct : Oct-96* : Nov : Nov : Nov : Nov : Dec : Dec : Dec : Dec : Jan :20 1, Jan :38 1, Jan : Jan : Feb :10 1, Feb : Feb-96* : Mar :51 1, Mar : Mar : spring tides to ensure that we also sampled days during maji mafu and to test for a seasonal effect. Sampling in Matemwe was conducted during four periods corresponding to the middle of the two monsoon periods (December and June) and the two transition periods (March and September). In each of these periods, we sampled every other day (seven days) within a fortnight between the day of the new moon (day 1) and the day of the full moon (day 14 or 15). Thus we tracked landings from the peak of one set of spring tides during the new moon, through the weakening tides, the neap tides, the building tides to the peak tides of the full moon. We did not sample the fortnight following the full moon. In the months between these four periods of intensive sampling, we augmented the core sampling with one additional day per month one day during the new moon and one during the full moon spring tides. This emphasis on bamvua was to obtain more observations of the much higher daily landings during this phase of the moon. We classified the day of landings with respect to the lunar tides by noting day 1 as the day coinciding with the astronomical prediction of the day of the new moon. Subsequent days were labelled sequentially through day 28 or 29 until day 1 of the next lunar cycle. Estimation of each landing Individual landings were weighed as the fishermen disembarked their vessels and approached the auction. We weighed the catch to the nearest kg on a spring balance suspended from a tripod. While weighing the fish, we noted the time, and obtained details on gear and vessel type. Species composition was resolved to taxonomic family in the field (Bianchi, 1985). Unknown species were

6 24 N.S. JIDDAWI ET AL. collected for later identification when possible. Some fishes were recorded as unknown. We categorised each string by the dominant family. If more than one family were present on the string, the most dominant by weight was classified as family #1. The second and third most dominant types were recorded as families #2 and #3. If more than three families were present on the string, #3 was called mixed. ThusV with respect to results reported in this report, for each landing we recorded: time of landing; vessel type (or on foot); number of crew; type(s) of fishing gear used; total weight of each fish string with each string classified as #1, #2 and #3 families (or mixed ); and total weight of fish for the landing (sum of all strings and individual fish). We also obtained counts of specimens when possible, but, with the exception of the octopus landings (Mhitu & Jiddawi, 1999), these data were incomplete. Landings of medium to large catches of small fish could not be counted accurately without interfering with the marketing process. Number and size composition information would be more easily obtained by sub-sampling from among the landings, perhaps on different days from the landings estimations. RESULTS Sample size effects on estimating annual landings Our objective with this document is to provide pragmatic guidance on designing a survey to estimate landings for a specific site. We use the data collected from Matemwe and Mkokotoni as an example of how to develop a survey that is easily applied in the field and provides robust estimates of total landings and associated variance with a minimum of computation. As mentioned above, the data collected at Matemwe and Mkokotoni are biased by purposive selection of sampling days from the bamvua periods. Nevertheless, the data can still be used to estimate sampling requirements and the structure implies that sampling efficiency can be improved by stratification. Figure 2 a d shows landings by lunar day with a smoothed trend line fit by LOESS (Cleveland, 1985). The LOESS line provides a local estimate of the mean landings conditional on the lunar day. Two plots are presented for each village, one starting on lunar day 1 and the second on lunar day 20, to examine the effect of the endpoints on the LOESS fit. The strong correlation in Matemwe between landings and lunar day is obvious regardless of the startpoint of the fortnightly period. The LOESS fit implies a maximum ratio of about 7:1 in total daily landings between bamvua versus maji mafu periods. The effect is much weaker in Mkokotoni. The strong periodicity indicated at Matemwe suggested that, at a minimum, stratifying sampling days into high and low yield categories might increase the precision of survey estimates. While the LOESS procedure and the classification of sampling days as lunar days allowed us to accommodate the lunar periodicity of landings in this analysis, we note that an alternative would be to use a periodic regression model (I.V. N. Bell, pers. comm., see Bell, 1997). The strong fluctuations in landings in Matemwe largely resulted from the reduced activity during days of weak tides (Jiddawi & Stanley, 1999b). Few vessels leave for the fishing grounds and the number of fishermen per boat declines. It is more difficult to get over the fringing reef, especially for the larger vessels. Secondly, since the daytime low tides are not as low during the neap period, fewer octopus are exposed for hand-collection on the coastal fringing reef and the reefs around Mnemba atoll, the principal fishing ground of Matemwe fishermen. Thus fewer octopus fishermen pay to accompany the vessel owners, thus further reducing the incentive to travel to the grounds. We explored survey sampling (Cochran, 1977) as a basis for computing estimates of total annual landings. Randomisation provides a repeatable means of avoiding bias in the selection of sampling days, and survey estimators are distribution free in that it does not require assumptions about the statistical error distribution. For the standard survey designs considered here, the estimators are widely published (Cochran, 1977; Thompson,

7 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR 25 a b Catch (kg) c d Lunar day Fig. 2 a d. Plots of LOESS fit to Matemwe (a,b) and Mkokotoni (c,d) daily landings data with a comparison of different start points of day 1 (a,c) or day 20 (b,d) within the lunar month 1992) and easily computed. Furthermore, the collection of data following a survey sampling scheme does not preclude the use of alternative methods of data analysis. For example, Jiddawi and Stanley (1999b) used an ad hoc procedure to estimate mean landings for each lunar day. These were then summed to obtain total landings for a lunar period with a bootstrapped estimate of variance. This ad hoc methodology could be applied to situations in which the data were collected using a survey sampling design. What if the observed data were actually a survey sample? In the simplest case, we could assume that the sampling days were selected at random with known probability to investigate the corresponding estimates of total annual landings. For example, suppose that the 63 days sampled over two years at Matemwe were actually a simple random sample from a given year. Let y i be the observed landing on a particular day. The estimator of the population total is given by

8 26 N.S. JIDDAWI ET AL. N yi (1) i= 1 ˆτ= Ny = n where n is the number of days observed and N is the number of days in the year. The estimated variance of ˆτ is given by (2) where (3) The resultant estimates of total landings, assuming simple random sampling, are provided for Matemwe and Mkokotoni (Col. 3, Table 3) Continuing with the exercise, the variance estimates can be used to prime calculations of the sample size required to achieve a specified absolute or relative precision. In other words, for landing sites similar to Matemwe and Mkokotoni, researchers can calculate before the study the number of days of sampling per year that would be required to estimate annual landings within specified targets for precision. The sample size for relative precision can be computed using: (4) s 2 = n i= 1 n = r z γ v N s 2 2 N n (ˆ) τ = n N ( y y) i n n N 2 where r is the relative error, z is the upper α/2 point of the standard normal distribution, and γ = σ/µ. This formula was applied to the data for both villages for a range of values of relative precision given the estimated variance to yield the curves shown in Figs. 3 a d. These figures indicate the number of sampling days required to meet a targeted level of precision for Matemwe and Mkokotoni. With these curves, researchers can either determine how many sampling days are required to achieve targeted precision or, conversely, can determine what precision to expect for a given number of sampling days. As these curves relate specifically to Matemwe and Mkokotoni, researchers would require a test data set for other landing sites for precise determination of required sampling levels. Note that fewer sampling days are required for Mkokotoni to achieve the same relative precision. This is because of the much higher variance among landings in Matemwe, related to the strong correlation to lunar day (Table 4). The exercise can be extended to examine the likely effects of stratification by lunar days that typically have high or low landings. Based on Fig. 3, we assigned lunar days (1 5, and 25 29) to a high landings stratum while the remaining lunar days were assigned to a low landings stratum. Under stratified random sampling, the estimator of the population total is given by a weighted sum of the stratum means (5) L ˆτ st = Ny i i i= 1 Table 3. Estimates of annual landings for Matemwe and Mkokotoni based on the assumption of simple random sampling and stratified random sampling from data collected March 1995 March 1997 Simple random Stratified random Village Estimate sampling sampling Matemwe (n=63) Mean daily landings t t Total annual landings t t (95% confidence limits) ( t) ( ) Mkokotoni (n=49) Mean daily landings t t Total annual landings t (95% confidence limits) ( t) ( t)

9 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR Matemwe a Matemwe b Estimated sample size Mkokotoni c Mkokotoni d Absolute error Relative error Fig. 3 a d. Estimated sample required under assumption of simple random sampling to achieve target absolute (a, c) or relative (b, d) precision for Matemwe (a, b) and Mkokotoni (c, d). The estimated variance of y st is given by (6) L v N N i ( ˆτ n st )= i 2 N i= 1 where s 2 i is the within-stratum sample variance in equation (3). The estimates resulting from application of equations (4) (6) are listed in column 4 of Table 3 for both villages. Note the reduction in estimated annual catch for Matemwe. The stratification has removed much of the bias caused by additional sampling during bamvua days. The effect is less evident for Mkokotoni i si 2 n i i Table 4. Number of days required to achieve precision target in estimating annual landings for Matemwe and Mkokotoni Precision target Village Estimator (n = number of days) ± 10% ± 25% Matemwe Simple random (t. ldgs=179 t) sampling Stratified random sampling Mkokotoni Simple random (t. ldgs=289 t) sampling Stratified random sampling

10 28 N.S. JIDDAWI ET AL. because of the much weaker variation associated with lunar day. The efficiency of a complex sampling design (adding the stratification) can be measured by using the ratio of the variance that would be obtained from a simple random sample of n sampling units to the variance obtained from a sample of size n using the complex design. The ratio is called the design effect (deff), and is a measure of the change in precision achieved by using the more complex design instead of a simple random sample. Design effects were developed for the purpose of estimating the sample size needed for a complex survey. If an estimate of the sample size required for a simple random sample is available, one can multiply that sample size by the deff to obtain the sample size needed with the complex design (Lohr, 1999; Snedecor & Cochran, 1980). This method was used to compute the results for sample size requirements under stratified random sampling that are shown in Figs 4 and 5. For Matemwe, the deff under stratified random sampling was estimated to be 0.69 (867.21/ ), while for Mkokotoni, the deff was (2021.1/1970.3). Thus, a gain in precision (reduction in variance) due to stratification of about 30 % should be achieved at Matemwe, while there is no gain in precision due to stratification at Mkokotoni. Alternative stratification schemes may yield different results. For example, changing the choice of days corresponding to either bamvua and maji mafu could increase efficiency. Other stratification categories could include day of the week. Fridays, the day of prayer, typically has few landings. Weekend landings were reputed to yield higher prices, which might act to increase landings. Monsoon or seasonal effects could also be useful for stratification. The calculations of the previous section provide a survey manager with the means to approximate the sample size required to attain a specified level of precision. Application of standard results from survey sampling to the observed data is flawed since the peak landings days were oversampled and were not randomly selected. The stratification of lunar days into high and low landings days was post hoc, and thus a penalty should have been paid for choosing strata after observing the data (Cochran, 1977). For planning purposes, however, the observed data are the best available, and it is reasonable to assume that they can be used to prime sample size calculations for future surveys Estimated sample size Estimated sample size Bound on estimation error Fig. 4. Estimated sample required under assumption of stratified random sampling to achieve target absolute precision for Matemwe Bound on estimation error Fig. 5. Estimated sample required under assumption of stratified random sampling to achieve target absolute precision for Mkokotoni

11 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR 29 Simulating a virtual year In this section, we conduct a simulation exercise to examine the expected behaviour of estimates of total landings and the associated confidence intervals over repeated sampling. The idea here is to use the observed data to create a virtual year of landings based on lunar days. This exercise allows survey managers to explore the actual variability about a true answer that one can expect given the constraints on sampling resources, that is, the number of days. To conduct the simulation, a population of annual landings was constructed using the observed data from each village. We wanted a complete year of landings from which to draw a typical sample. To construct the virtual year we assumed that there was no difference in mean landings among years or lunar periods within the year within each village. We also assumed that the mean landings did not differ between the two peaks in a lunar period. In practice, there may be a difference in the magnitude of the two peaks, and indeed seasonal effects. However, the data in hand did not permit detection of these differences. The year was assumed to be 365 days in length, and each day was assigned a lunar day from 1 28 in sequence (Note: while there are actually some day Catch (t) Julian day Fig. 6. Example of simulated year for Matemwe 29s within a calendar year, they were ignored within the simulated year). We chose to simulate 1997, when January 1 corresponded to lunar day 22: Accordingly, January 2 was assigned lunar day 23, January 7 lunar day 28, and January 8 lunar day 1. Each of the observed daily landings was assigned a lunar day from 1 to 28. For each day of the year, a landings observation corresponding to the lunar day was drawn at random. For some lunar days, there were no observed landings. For these days, each lunar day from 1 to 14 was paired with a lunar day from 15 to 28. Thus, the pairs (1, 15), (2, 16), through to (14, 28) were formed. When landings were not observed for one member of the pair, a random draw was taken from the landings observed on the other member day. If both days lacked data, the draw was taken from days with data immediately prior to and following each member of the pair. In Matemwe, for example, landings were not observed for lunar days 7 or 21, thus data corresponding to lunar days 6, 8, 20 and 22 were pooled and a landings observation selected at random. The result was a population frame for each day of the calendar year, conditioned on lunar day. An example of a virtual year of landings is shown in Fig. 6 for Matemwe and Fig. 7 for Mkokotoni. Note that for Matemwe, the Catch (t) Julian day Fig. 7. Example of simulated year from Mkokotoni

12 30 N.S. JIDDAWI ET AL. simulation correctly predicts consistently low landings every two weeks, corresponding to maji mafu, but the intervening period can be highly variable. Days of large tides can provide large landings but also low landings owing to a lack of fishing effort through inclement weather or sociocultural reasons. There is less relative variability in the Mkokotoni landings and fewer very-lowcatch days. Having created a simulated year of landings that correctly depicts the magnitude, and variance of daily landings, we could treat a simulated year as a complete sampling frame. We then sampled from it to explore how varying sample size influences the precision of our estimates of annual landings for the two villages. The performance of random sampling can be examined by repeatedly drawing a sample from the virtual year using a Matemwe A B Sample Total catch Fig. 8. Example of 50 simulations of annual estimates of total landings for Matemwe based on simple random sampling 25 (A) or (B) 100 days per year Matemwe A B Sample Total catch Fig. 9. Example of 50 simulations of annual estimates of total landings for Matemwe based on stratified random sampling 25 (A) or (B) 100 days per year

13 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR 31 particular design. For example, Fig. 8 a b shows the results of 50 simulations of visiting the Matemwe landings 25 or 100 times during a year (averaging about 2 or 8 times/month) under the assumption of simple random sampling. The solid vertical line represents the true total annual landings. The estimated total is indicated by the solid circle and the horizontal lines represent 95 percent confidence intervals. Similarly, Fig. 9 a b shows the results of 50 realisations of a stratified random sample approach. Note that the distribution of the 50 estimates is closer to the true value in the 100 sampling days per year case. But also note that even for the high sampling effort case of 100, an individual estimate of total landings for the year can occasionally vary significantly from the true value. The simulation even illustrates the case where the 95% confidence range for an estimate may not include the true value. The simulation of sampling from the virtual year provides a heuristic tool for showing how much estimates can vary from a known value. The precision curves shown in Figs 4 6 show the average effect one can expect for a given level of sampling effort, however, any individual estimate can vary widely from the true value. Investigators can use these exploratory tools in combination with the daily estimates of costs for a mobile sampling team to explore the tradeoff between obtaining more precision for a given site against how many sites a team could study for the same expense. DISCUSSION The limited fisheries budgets in Zanzibar preclude successful implementation and maintenance of comprehensive census-style capture of harvest and fishing effort information (Othman, 1999; Kombo, 1999; Jiddawi and Stanley, 1999a). This is consistent with experiences elsewhere in the world (Wright and Hill, 1993). As stated by Munro and Fakahau (1993): Attempts have been made to establish statistical systems to monitor the artisanal fisheries in many tropical countries but we know of no example of sustained successful implementation. The problems of dispersed landings, the multitude of species, variations in fish prices and of unrecorded subsistence catches normally combine to make the systems inaccurate and inordinately expensive in terms of manpower. Additionally, the fact that most tropical fisheries are also multigear fisheries makes the derivation of any but the crudest expressions of fishing effort almost impossible. The result has been that few meaningful or beneficial results have ever been perceived to emerge from statistical systems in multispecies, multigear fisheries, and there has been strong tendency to scaledown the work to meaningless levels or abandon it altogether. We have demonstrated that a mobile sampling team would provide cost-effective landings data. We suggest that, with minor modifications, the same argument can be applied to the estimation of additional fishery details such as species composition and catch rates. These data would provide unbiased fishery monitoring data that could be used to supplement broad-based censusstyle programmes. With landings estimates, the data provide the minimal basis for stock for stock abundance monitoring (Dalzell, 1996; Jennings & Lock, 1996; Appledoorn, 1996; Pet-Soede et al., 2001). One sampling team could study a number of index sites. While the long-term commitment of a 3- to 4-member team and vehicle would seem costly, so are the resources needed to maintain samplers in every village. The sampling costs are small relative to the return in improved stock assessment from better quality data. Larger, more complex, landing sites such as Zanzibar Town, may be more problematic owing to the highly dispersed landings. However we suggest the principle discussed here could be applied, although the costs might be higher. We suggest a number of benefits would accrue from adopting an index site approach for estimation of fishery statistics in the state of Zanzibar or elsewhere in smallscale fisheries. Firstly, accurate landings estimates for specific index sites can be used to complement a broadly designed census-style survey, such as currently exists in the state of Zanzibar. The index sites can be employed within a layered sampling scheme and be used to calibrate the general sampling method or to provide detailed estimates of specific

14 32 N.S. JIDDAWI ET AL. fishery statistics not provided by the overall system. A sufficient number of index sites in conjunction with detailed knowledge of the number and relative size of all landing sites (the sampling frame), such as those available in Zanzibar (Hoekstra et al., 1990, Lyimo et al., 1997), could, by themselves, provide a stand-alone system capable of providing independent estimates of total landings on Unguja Island. These estimates would have known precision, and quality assurance would be easy to monitor because of the small staff. We also note that the sampling effort could be expanded with minor additional cost to include other data such as catch-per-unit-effort, gear type and species composition, as was demonstrated by Jiddawi & Stanley (1999b). In addition to complementing or even replacing the inaccurate census-style approaches, index sites could also be chosen and initiated for specific tactical reasons. Some index sites could be chosen as part of comparative studies to test the effectiveness of management actions such as gear restriction, and seasonal or area closures. Evidence for successful management will be essential if local governments wish to attract investment capital. While we argue that the cost of index sites is justifiable, we also recognise that the cost of such index sites would be non-trivial. If resources are not available nationally, we suggest that initiation and even continuing assistance might be maintained through foreign donor support, especially as part of the implementation of marine protected area (MPA) programmes. We hope that this document will facilitate the development of these programmes in that the techniques discussed will allow direct cost estimation for target precision levels. Finally, we emphasise that such in-depth monitoring of the landings will also generate better relationships with the fishing community. Not only is the development of a working relationship with the fishermen critical to the success of the monitoring but also is, in itself, a valuable outcome of the work. In many respects, it will be the interaction with the community, which will help re-shape and refine the objectives of the monitoring. We note that the fishing community of Matemwe emphasised the significant lunar periodicity in landings during the meetings at the beginning of the study. Thus fishermen s knowledge could have been used to optimise sampling design at the outset (see also Poizat & Baran, 1997). Finally, as with all studies and sampling programmes, users need to clearly define the objectives of the study prior to initiation. No single sampling programmes can provide answers to all questions. Acknowledgements We would like to thank the shehas (village heads) for their assistance and fishing communities of Matemwe and Mkokotoni for their cooperation and patience during the studies. Data collection activities were diligently conducted by H. Mhitu, J. Kangwe, K. Khatibu, and Y. Saleh, and the field work was supported by the Directors of the Fisheries Department and the Institute of Marine Sciences. We also thank Drs K. N. I. Bell and A. Whittick for their thoughtful and constructive reviews. REFERENCES Appledoorn, R. S. (1990) Model and method in reef fishery assessment. In: Polunin, N.V.C. & Roberts, C.M. (eds.) Reef Fisheries. Chapman and Hall, London. pp Bell, K. N. I. (1997) Complex recruitment dynamics with Doppler-like effects caused by shifts and cycles in age-at-recruitment. Can. J. Fish. Aquat. Sci. 54: Bianchi, G. (1985) FAO species identification sheets for fishery purposes. Field guide to the commercial marine and brackish water species of Tanzania. TCP/URT/4406 and FAO (FIRM) Regular Programme, Rome. Cleveland, W.S. (1985) The elements of graphing data. Wadsworth, Monterey, California. Cochran, W.G. (1977) Sampling techniques. 3 rd ed. Wiley and Sons. New York. Dalzell, P. (1996) Catch rates, selectivity and yields of reef fishing. In: Polunin, N.V.C. & Roberts, C.M. (Eds.), Reef fisheries. Chapman and Hall, London. pp FAO (1994) United Republic of Tanzania: Zanzibar Fisheries Investment Project Identification report. Report No: 13/94 ADB-URT 50, 4 February Hoekstra, T., Ngoile, M.A.K., Jiddawi, N. S. & Rotteglia, C. (1990) Census of marine fishing units of Zanzibar in 1989: Regional Project for the Development and Management of Fisheries in the Southwest Indian Ocean. AF/87/008/WP/60/90/E.

15 ARTISANAL FISHERIES STATISTICS IN ZANZIBAR 33 Jennings, S. & Lock, J.M. (1996) Population and ecosystem effects on reef fishing. In: Polunin, N.V.C. & Roberts, C.M. (eds.), Reef fisheries. Chapman and Hall, London. pp Jiddawi, N.S. & Stanley, R.D. (eds.), (1999a) Fisheries stock assessment in the traditional fishery sector: The information needs. Proceedings of the National Workshop on the Artisanal Fisheries Sector, Zanzibar September 1997, Zanzibar, Tanzania. Jiddawi, N.S. & Stanley, R.D. (1999b) A study of the artisanal fishery in the villages of Matemwe and Mkokotoni, Zanzibar, Tanzania. In: Jiddawi, N.S. & Stanley, R.D. (eds.) Fisheries stock assessment in the traditional fishery sector: The information needs. Proceedings of the National Workshop on the Artisanal Fisheries Sector, Zanzibar September 1997, Zanzibar, Tanzania. pp Kombo, A.H. (1999) Deficiencies in the past method of Zanzibar fisheries data statistics and prospects of the proposed plan. In: Jiddawi, N.S. & Stanley, R.D. (eds.) Fisheries stock assessment in the traditional fishery sector: The information needs. Proceedings of the National Workshop on the Artisanal Fisheries Sector, Zanzibar September 1997, Zanzibar, Tanzania. pp Lohr, S. (1999) Sampling: Design and analysis. Duxbury Press, Toronto. Lyimo, E., Juma, B. A., Bhai, I.O., & A.H. Juma (1997) Report of the fisheries frame survey conducted in Unguja and Pemba. Commission of Natural Resources. Ministry of Agriculture Livestock and Natural Resources, Zanzibar. 27 pp. Mhitu, H. & Jiddawi, N. (1999) The octopus fishery in Matemwe and Mkokotoni, Zanzibar. In: Jiddawi, N.S. & Stanley, R.D. (eds.) Fisheries stock assessment in the traditional fishery sector: The information needs. Proceedings of the National Workshop on the Artisanal Fisheries Sector, Zanzibar September 1997, Zanzibar, Tanzania. pp Munro, J.L. & Fakahau, S.T. (1993) Appraisal, assessment and monitoring of small-scale coastal fisheries in the South pacific region. In: Wright, A. & Hill, L. (eds.) Neashore marine resources of the South Pacific. Institute of Pacific Studies Suva. pp Othman, A.M. (1999) An overview of the status of the Pemba Island fishery. In: Jiddawi, N.S. & Stanley, R.D. (eds.) Fisheries stock assessment in the traditional fishery sector: The information needs. Proceedings of the National Workshop on the Artisanal Fisheries Sector, Zanzibar September 1997, Zanzibar, Tanzania. pp Pet-Soede, C., van Densen, W.L.T., Pet, J.S. & Machiels, M.A.M. (2001) Impact of Indonesian coral reef fisheries on fish community structure and the resultant catch composition. Fish. Res. 51: Poizat, G. & Baran, E. (1997) Fishermen s knowledge as background information in tropical fish ecology: A quantitative comparison with fish sampling results. Env. Biol. Fish. 50: Ruddle, K. (1996) Geography and human ecology of reef fisheries. In: Polunin, N.V.C. & Roberts, C.M. (eds.) Reef fisheries. Chapman and Hall, New York. pp Snedecor, G.W. & Cochran, W.G. (1980) Statistical methods, 7 th ed. Iowa State University Press, Iowa. Thompson, S.K. (1992) Sampling. Wiley and Sons, Toronto. Tobisson, E., Andersson, J., Ngazi, Z., Ryberg, L. & Cederlof, U. (1998) Tides, monsoons and seabed: Local knowledge and practice in Chwaka Bay, Zanzibar. Ambio. 27: Wright, A. & Hill, L. (eds.) (1993) Nearshore marine resources of the South Pacific. Information for fisheries development and management. Institute of Pacific studies, Suva, Forum Fisheries agency, Honiara. Int. Centre for Ocean Development, Canada.

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