Tanga Fisheries Analysis - IUCN

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Appendix 1 - Vessel and Fishing Gear Descriptions Table 21 - Vessel Codes TCZCDP Vessel Code Ki-Swahili Vessel Name Description MS Mashua Wooden sailing vessel, with transom stern (to 10m) NG Ngalawa Dug-out canoe with outriggers (to 8m) DH Dau Wooden sailing vessel, with pointed stern and prow (to 10m) DC Mtumbwi Dug-out canoe without outriggers (to 3m). BT Boti A motorized vessel of any size/type Table 22 - Fishing Gear Codes TCZCDP Gear Code <strong>Fisheries</strong> Division Gear Code Gear (Ki-Swahili / English) Ch Di * Chupa za Kuzamia/Diving FF FF Fish-Fence Ja SN Jarife/Sharknet/Seine Net Ka LL Kaputi/Longline Ki SC Kimia/Scoop-Net Ku Di * Kuzamia/Senga/Diving Ma TP Madema/Fish-Trap Mi HL Mishipi/Handline Ny GN Nyavu/GillNet Ri RN Ringi/Ring Net Si SN Sinia/Sharknet/Seine To SP Tomo Kuchokoa/Spear Wa WW Wachokoaji 50
Appendix 2 - Description of Pair-wise Comparisons ANOVA (with single factor) was used to evaluate the presence of significance differences between groups (years) with the null hypothesis that H 0 : µ 1 = µ 2 = µ 3 = µ 4 = µ 5 . Where ANOVA indicated a significant difference a multiple comparison Tukey Test was employed, with the Kramer modification for unequal sample size (Zar, 1999, p212), to determine between which groups’ population means a difference existed. The data were tested for normality using both Excel Descriptive Statistics and Systat (V.10.2) using the Kolmogorov-Smirnov one-sample test reporting skewness and kurtosis. Skewness is ‘a measure of the symmetry of the distribution about its mean’ (Systat) and kurtosis indicates the length of the ‘tails’ in relation to what one expect in a normal distributed dataset. The data does appear to show some deviation from normality revealing a positiveness skewness (a long right-tail) and positive kurtosis (longer tails than expected). In this case a nonparametric analysis of variance (e.g. Kruskal-Wallis Test) might be considered appropriate but Zar (1999) indicates that a singlefactor analysis of variance (such as is proposed here, the single-factor being CPUE): ‘…is also robust with respect to the assumption of the underlying populations’ normality. The validity of the analysis is affected only slightly by even considerable deviations from normality (in skewness and/or kurtosis), especially as n increases.’ (Zar, 1999, p185) The analyses were initially performed in Excel spreadsheets. The means are sorted in order of increasing magnitude and then pair-wise differences, X B − X A , are tabulated (Zar 1999, p211). Equation 1 presents the calculation of the standard error, used in this test. Equation 1: Standard Error (Kramer, 1956; Tukey, 1953) SE = 2 s ⎛ 1 1 ⎞ ⎜ + ⎟ 2 ⎝ n A n B ⎠ Where s 2 is the error mean square from the analysis of variance of the complete data set (across groups) and n is the number of data in each of groups A and B. Equation 2 presents the calculation of Tukey’s ‘q’ value. Equation 2: Calculation of Tukey's 'q' value q = Where X A X SE B − B A X , X are the means of each group. If the q-value is equal to or greater than the critical value, q α,v,k , then the H 0 : µ B = µ A is rejected. This critical value is called the ‘Studentized range’, abbreviated q, and is dependent on α (the significance level), v (the error DF for the analysis of variance), and k (the total number of means tested) (Zar, 1999 p211). 51
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IUCN Eastern Africa Regional Progra
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The designation of geographical ent
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9. References and further reading .
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Acknowledgements I would like to th
Page 9: approximately 30% between 1996 and
Page 12 and 13: • The local Central Coordinating
Page 14 and 15: 4. Summary Description of Previous
Page 16 and 17: Mishipi Madema Nyavu RingNet Jarife
Page 18 and 19: Figure 3. Table 8 - Landing Sites S
Page 20 and 21: Table 9 - Comparison of CPUE data f
Page 22 and 23: Figure 4 - Fisheries Data Collectio
Page 24 and 25: the fish levy at the market place.
Page 26 and 27: • Data for spatial analysis over
Page 28 and 29: profile of the sampled data was not
Page 30 and 31: 7.2. The Spatial and Temporal profi
Page 32 and 33: increased catch per trip (per gear)
Page 34 and 35: Figure 13 - Mean CPUE (Kg/Gr/Hr, wi
Page 36 and 37: Figure 15 - Mean CPUE (Kg/Gr/Trip,
Page 38 and 39: Figure 17 - Mean CPUE (Kg/Gr/Trip,
Page 40 and 41: 7.3. Catch Composition The 1 st Pro
Page 42 and 43: Figure 19 - Proportion of Monthly C
Page 44 and 45: 7.4. Variations in Price of Fish by
Page 46 and 47: produced of mean catch per trip aga
Page 48 and 49: Figure 27 - Mean Price against Mean
Page 50 and 51: 7.5. The Use of Different CMAs by L
Page 52 and 53: Figure 28 - Sampled Fishing Activit
Page 54 and 55: Table 19 - Objectives/Result Areas
Page 56 and 57: Table 20 - Summary Table of Results
Page 58 and 59: 8.3.3. Fishing Gear Definitions The
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