Accurate and long-term fisheries data are vital for fisheries managers to understand and

manage fisheries effectively. Only a few inland waters have such data. This is often due to the

difficulty and high cost as well as low priority given by governments to data collection activities.

Besides accuracy, practicality and cost are also important considerations when designing fishery data collection systems. A data set from the fishery of Jebba Lake in Niger State, North-central of Nigeria, was used to assess how low cost, yet accurate, fisheries data collection can be achieved. Simulations of reducing the frequency of the annual inventory of fishery manpower and crafts (frame survey) and of lowering the sampling effort of monthly fish catch and fishing activities (catch assessment survey) were made. The effect of the reductions on daily catch, fishing activity and yield estimate was then determined. Reducing the number of sampling days was reduced from four to two per month. Problems of obtained accurate and long-term fisheries data from tropical inland waters were discussed

Abiodun, J.A.

Oshungade, I.O.

Aquatic Commons

Introduction
Most methods and approaches for fisheries management and development require accurate
andreliable data collection (Quiros, 1998). The need for reliable fisheries data was highlighted
duringthe international conference on sustainable contribution to food security in Kyoto, 1995 and
hasbeenwidely recognized (Mandima, 1996).
Accurate and long-term fisheries data are vital for managers to understand and manage
fisherieseffectively. However, only a few tropical reservoirs have such data. This is often due to the
difficulty,high cost and the low priority given by the governments to data collection activities.
Besidesaccuracy, practicality and cost are also very important considerations when designing
fisheriesdata collection system.
. The lack of fisheries data therefore mean, any management activities must often be'
precededby tine consuming and costly data collection activities. These frequently required inputs,
discouragedonor agencies from funding fishery assistance projects in Nigeria. Low funding levels
of fisheries projects is also associated with the sector's poor track record in managing fisheries
causedby the implementation of poorly identified regulations that were not based on representative
data.
Several authors had highlighted the underlying problems of obtained good quality fisheries
datafor inland fisheries. The cost of fisheries surveys is very high due to the practical difficulties of
datacollection in villages around inland waters that are often fragmented and dispersed (Mandima,
1996)Du Feu (2003) stated that there is a tendency for reservoir fisheries to diversify as they get
older,thereby increasing data collection cost. Both the international and national funding agencies
frequentlyquestion whether the returns from fishery justify the high cost of fisheries surveys (Orach-
Meza, 1991). The data collectors are not motivated and poorly paid. The importance of data
collectionexercise is seldom fully appreciated by fisherfolk thereby making them uncooperative. <,..
The problems of data collection were evident around Lake Kainji, Nigeria. Following the hand
: :', w: :::; ..... ,',= :. tagl _,;= : =. :::,' :.=~= J
Abstract:
Accurate and long-term fisheries data are vital for fisheries managers to understand and
managefisheries effectively. Only a few inland waters have such data. This is often due to the
difficultyand high cost as well as low priority given by governments to data collection activities.
Besidesaccuracy, practicality and cost are also important considerations when designing fishery
datacollection systems. A data set from the fishery of Jebba Lake in Niger State, North-central of
Nigeria,was used to assess how low cost, yet accurate, fisheries data collection can be achieved.
Simulationsof reducing the frequency of the annual inventory of fishery manpower and crafts
(framesurvey) and of lowering the sampling effort of monthly fish catch and fishing activities (catch
assessmentsurvey) were made. The effect of the reductions on daily catch, fishing activity and yield
estimatewas then determined. Reducing the number of sampling days was reduced from four to
twoper month. Problems of obtained accurate and long-term fisheries data from tropical inland
waterswere discussed.
DATA COLLECTION IN AN INLAND WATER FISHERY
(JEBBA LAKE, NIGERIA): THE TRADE-OFF
BETWEEN COST AND ACCURACY
J.A. Abiodun, and I. O.Oshungade,
1. National Institute for Freshwater Fisheries Research. P.M. B 6006. New Bussa, Niger State,
Nigeria.
2. Department of Statistics, University of Ilorin, P.M. B 1515110rin,Kwara State.
The Lake
Jebba Lake is located in North central Nigeria. The lake (Fig.1) is man made and was
impounded in 1983 for the purpose of hydro electric power generation but with opportunities for
fishing, drawdown farming and navigation. The lake has a maximum length of 100km and
maximum width of 12km, and covers an area of approximately 1200 km2 (Varying with seasonal
fluctuations), a volume of about 13,200m3 and has a mean depth of 11m (max. 105m). It hasa
shoreline length of 74km with catchments area of 0.3x 106km2 (Ita et al 1983). The lake gets the
major bulk of its water supply from the discharge of Lake Kainji while tributaries like Rivers Eku,
Awun and others also discharge into it.
Fishery data of Jebba Lake was collected using Catch Assessment and Frame survey,
which were conducted between 2000 and 2006. The Frame survey involved going round the entire
lake once in a year to identify and count every fishing locality within the lake, the number offishers,
the crafts and fishing gears employed for fishing. Catch Assessment Sampling (CAS) involved a
detailed examination and recording of the content of a canoe that had just landed on return from
fishing trip using properly trained ADP enumerators. As a fisher land canoe on the beach, the
enumerators examined the fish caught, sorted them according to species and the gear type used,
recdrded the weights, number and prices of all the species of fish landed. Fishing time and the
number of canoes were also recorded to establish the catch-per-unit-effort (CPUE).
Reducing the Sampling Intensity
Annual frame survey data collected between 2000 and 2006 were used to determine the
effort on monthly yield estimations of reducing the frequency of the frame survey to alternative
yields. For each year and gear type, the number of gears from the frame survey was replaced by
the average number of gears recorded during the frame surveys conducted the year before and
.:(' year after.
Replace estimate = NY•1+NY+1
2
Where: Ny., =estimate for the year preceding the year of interest (Ny)
Materials and Method
t:: ;; .::_::::: ;:
over of the monitoring system by the FAO/UNDP Lake Kainji Research Project (1968 to 1974) and
prior to the start of the Nigeria - German Kainji Lake Fisheries Promotion Project in 1993, only two
fisheries surveys (Ekwemalor, 1975) and six months of fish catch assessment surveys
(Ekwemaor, 1997) were undertaken by the project.
Various methods have been tried to lower the costs and optimize the continuation of
fisheries data collection. Costs were reduced in Lake Volta, Ghana, by confining sampling of fish
catch and effort to one portion of the lake (Bazigos, 1970). The yield estimate from the smaller area
was then raised to the total lake area to produce lake-wide estimates. The disadvantage of this
approach is that differences in fishing practices are likely to exist between lake areas causing
errors when extrapolating yield data. In lake Kainji fishery, Bazigos -(1972) recognized that the
oath-per-unit effort (CPUE; kg fish net') from experimental gill net trial fishing and actual gill net
catches by the fisherfolk were related. He used this, with projected figures for fishing effort (total
number of canoes multiplied by frequency of fishing), to estimate total fish yield. The disadvantage
is that experimental gills nettrial fishing is as costly to administer as catch assessment surveys (Ita,
1998). Furthermore, a relationship between gill net trial catches and total lake yield did not exist
beyond 1972 due to the increased number of equal yielding fishing gear type is used (du Feu,
2003).
The objective of this study is to investigate an alternative way by which the cost of fisheries
frame and catch assessment surveys can be reduced by optimizing sampling strategies. Adata set
from the fisheries surveys conducted for the Lake Jebba fishery between 2000 and 2006 has been
used to assess how this might be achieved. It is anticipated that the findings will help to strengthen
the data collection of the Jebba lake fisheries, as well as guide data collection activities within
similar reservoir fisheries. This may lead to increased understanding and therefore better
managementoffisheries in tropical inland waters.
ReducedSampling Intensity of the Catch Assessment Survey
Sampling Intensity
For the original four-day sampling, the highest sampling intensity for fishing activity
wasnoted for those fishing gears that were evenly distributed throughout the sampling stations
(suchas longlines and gill nets). The intensity was also high for the gears which, although hav~ a
morescattered distribution, had many sampling stations in areas where they occur (drift nets.) This
isunliketraps, which had a scattered distribution, but less well represented (Table 4).
ReducingSampling Intensity -effecton total monthly fish yield
The difference between the estimates recorded for full data set (four days sampling)
and the estimated calculated for the various reductions increased as the sampling intensity
reduced.
Resultsand Discussion
ReducedFrequency of the Frame Survey
The new frame survey estimates for fishing gears calculated from the mean of the
adjacentyear 'actual' frame survey in Table 1 are given in Table 2. The difference in percentage of
the'replaced' estimates from the actual estimates is given in Table 3. The percentage difference
betweenthe replaced totals and the actual frame total varied between + 94% and - 32% (mean: -
2.8%, std: 21). The largest difference was noted for gear types in which the number of gears did not
riseuniformly between years.
The yield estimates, that were re-calculated for each fishing gear types using the replaced
framedata in Table 2, produced a range of errors from the actual monthly yield estimates of between
+ 15% and -19% (mean: 1 3%)
Undertaking frame survey every two years, rather than annually, reduced the survey cost by
half.An advantage in lowering the sampling intensity of frame surveys was that fishing activity and
CPUE estimates, the main indicators measured by the catch assessment survey, remained
unaffected. .
The Jebba Lake example, however, indicated that reducing the number of frame survey
should only be considered when the number offishing gears is either constant or increase/decrease
ata constant rate. For example, sudden changes resulting from migration in or our if the fisher, he
developmentof new fisheries or implementation of management regulations will cause annual
gearsnumbers to fluctuate and so affect the accuracy of final yield estimates. However, the
collectionof fisheries data is usually undertaken in order to evaluate or monitor impacts of
managementintervention. Changes in annual gear numbers are therefor often to be expected.
For the Jebba Lake fishery, there was a large variation in gear numbers recorded each year.
Thiswas caused by a rapid decline in number of gears owned by fishfolk. A reduction in the
frequencyof the annual frame survey is therefore not recommended. The case for not reducing the
numberof frame surveys is supported by Mandima (1996) for Lake Kariba who suggested that the
frequencyshould be increased sue to the constantly changing fishery that caused annual frame
surveytoquickly tecome outdated.
:)__--_-,_* 5-~:::
.t.NY+1 Estimate for the year after the he year of interest (Ny)The replaced figures for the numbers were substituted for the actual estimated, after which
themonthly yield for each gear was calculated. The impact of reducing the sampling intensity ofthe
catchassessment su rvey was also assessed using the data set from four days per month sampling
(January2002 to December 2003). The sampling intensity was reduced and the effect on the
monthly ield, CPUE and fishing activity assessed.
Results from two fishing gear types were used to demonstrate the effect of reduced
sampling.These were gill net fishery, which has a low CUPE, a low variance in catch and a high
coveragethroughout the lake and the long line fishery that has a high CUPE, a high variance in cath
andascattered distribution. The mean percentage difference for all months between the two sets of
resultand the original full data set was calculated. To prevent negative positive signs from canceling
eachother all errors were treated as positive.
In light of the diversity of inland water fisheries and variability caused by a reduction in the
number of sampling stations, it is recommended that a high number of sampling stations and low
number of sampling days in each of stations is used. This agree with a general theory of sampling,
which states that, the variability between sampling stations is almost always greater than within the
stations themselves (Lohr 1999).
The used local villagers (rather than salaries staff from the public sector) as 'data recorders
appears to be increasing. Examples where village based recorders have been used included the
Bangweulu Swamps, Zambia, Sri Lankan data may reduce the problems of the low motivation of
data collectors from the public sector, the dual role of officers being enforcers of fisheries rules and
regulations and data fishfolk. Greater participation by fishfolk may also lessen data fabrication by
recorders and so make further reductions in sampling intensity and cost possible. For this to be
achieved, proper selection and training of recorders and subsequent supervision, spot checks and
data verification must be undertaken. Participation of villagers also supports the idea of
community-based management approach and the provision of village based employment.
Any recommended sampling strategy depends on fishfolk giving accurate information to
data collectors. This may prove difficult, especially when fishfolk associate data collection with
taxation. Fisherfolk must therefore understand the need for accuracy when responding to survey
questions.
Conclusion and Recommendation
Reducing Sampling Intensity - effect on CPUE and fishing activity be gear type
As well as investigating the effect that reducing sampling intensity has on total yield,
it was also important to assess the effect of the reduction on the components of the yield equation,
namely fish catch (CPU E) and fishing activity. For all reductions in sampling intensity, the resulting
errors in estimate of CPUE were almost double that offishing activity. Larger errors were observed
in the longline compared to gill net fishery, especially in the case of CPUE.
In both gear types, the error increased as sampling intensity decreased. The largest error
was error was noted when sampling months and stations were reduced. Lower costs for
supervision can be expected when the two days sampling each month follow each other. Sampling
during consecutive days caused a higher error in CPUE and activity than that noted when two
sampling days were separated.
Reducing the number of sampling stations gave the largest increase in error, and reducing
from four to two days sampling per month caused less error. This suggests that greater accuracy of
the survey may be achieved by increasing the number of sampling stations (increasing the area
coverage) and that the extra cost of this may be offset by decreasing the number of sampling days
in each. Given that the estimates of CPUE were more affected than those of fishing activity, the
optimal sampling strategy has been calculated using the number of CPUE records.
The negative and positive error between the estimated yield from the separate gear types and
months cancelled each other and caused little difference to the final estimated 3.4% for reduction
from four to one day sampling. Two replicate reductions were made with mean error from the
resulting data sets being used. Using alternate months and slightly fewer sampling stations
produced the higher error. Reducing the number of sampling days has less effect, with error
increasing by 2.5% for each reduction of one sampling day per month.
The total cost of the survey using the full four days sampling was N120,OOOper month. The
amount includes per diem and transport costs for supervisors and field recorders and cost data
checking, input and compilation. This was reduced to about N72,OOO(40% reducing from four
days to two days sampling.
cWhere:
GN = gill nets, On = drift nets, CN = cast nets, LL = long lines and TR = fishing traps
855
1.4
197
0.3
274
3.9·
31
0.4
165
7.0
8
0.3
48
2.7
9
0.5
476
2.2
60
0.3
2,332 195 694 925 4,623
21,250 1,750 2,375 7,000 61,125
Number of records
Percentage cover
Fishingcatch
Samplin~ effort (4days sampling/month)
Fishingactivity Number of records
Percentage cover
Totalnumber of existing gears
Meanmonthly fishing effort
(Totalgears fished every month
GNGNGNGNGN
Table4: Total number of fishing gears 9from 2003 frame survey) and total monthly fishing
effort (total number of gears fished every mon.tp) and the percentage sampled for
fishing activity and fish catch using four days per month during the 2003 catch
assessment survey.
17945432005
-7-321326192004
-37-15-17-202003
-1128-8-2182002
2
TR
-18
LL
5
eN
39
ON
2
GN
2001
YEAR
Note: The number of gears has been rounded independently to the nearest 10.
WhenGN = gill nets, ON = drift nets, CN = cast nets, LL = longlines and TR = fishing traps
Table3: The percentage difference between the 'replaced" and actual estimates of the
number of gears.
3,4548353591251,3042005
3,7896785181581,8022004
4,469985.5891621,8652003
4,3331,2476641572,3902002
4,125
TR
1,294
LL
665
eN
164
ON
2,504
GN
2001
YEAR
Table2: The "replaced" frame survey estimates of the total number of fishing gears on
Jebba Lake using the running mean of adjacent years frame survey figures
282167012601261 0892006
2,9044323431211,2712005
4,0871,0004581251,5192004
4,623
4,852
924
971
694
720
195
197
2,332
2,210
2003
2002
4,0421,5696381182,4482001
3,397
TR
1,632
LL
612
eN
130
ON
2,798
GN
2000
YEAR
C ::- :: ::::: :' ::. :::.:: :: >~:~:::- =: =34.: '7 ,,:::1
Table1: Total number of fishing gears on Jebba lake from actual frame survey
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Paper. No. 374. Rome FAO: 91-118.
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Fisheries management. I. G. Cowx ed. Fishng News Books. ISBN 0 85238 1778:323-334.
Mandima,J. J. (1996). Limitations of catch and effort data collection methods used for inland
fisheries in the SADC region, with particular reference to Zimbabwe. In: Stock
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085238224327:368-77.
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Grove, california 93950, USA.ISBN 0534353614. 494pp.
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..:t


Data collection in an inland water fishery (Jebba Lake, Nigeria): the trade-off between cost and accuracy

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Data collection in an inland water fishery (Jebba Lake, Nigeria): the trade-off between cost and accuracy

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