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Data collection for stock assessment and fishery management

Data collection for stock assessment and fishery management. FMSP Stock Assessment Tools Training Workshop Bangladesh 19 th - 25 th September 2005. Purpose of talk.

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Data collection for stock assessment and fishery management

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  1. Data collection for stock assessment and fishery management FMSP Stock Assessment Tools Training Workshop Bangladesh 19th - 25th September 2005

  2. Purpose of talk • To present the data needs of the different FMSP stock assessment tools, and give some guidelines on data collection for stock assessment • Limited coverage in Section 3.2 of FAO Fish. Tech. Pap. 487 • Other references provided • Note – there are no special data collection methods or forms for LFDA, CEDA and Yield – general methods apply • Special forms are provided for collecting interview data for ParFish See Section 3.2 of FTP 487, and ParFish toolkit

  3. Content • Why collect data? • Useful references on data collection • ------------------------------------------------------------------------------------ • Data commonly used in stock assessments (e.g. using FMSP tools) • Data needs of the different SA approaches and FMSP tools • ------------------------------------------------------------------------------------ • Data collection methods (C/E, LF, biological, ParFish) • System design • Sampling design • Data forms • Database systems

  4. Why collect data? • 1. To provide information needed by policy makers and to meet international reporting obligations • 2. To evaluate likely benefits & costs of alternative management options (long term, strategic SAs) • 3. To monitor if you are achieving your goals and provide short term management advice (tactical SAs)

  5. Useful sources of information on data collection • Bazigos (1974) – Survey design, maths etc (for inland waters) • Caddy and Bazigos (1985) – Adaptation for manpower limited situations • FAO (1997) – General guidelines on data collection for management • FAO (1998) – New guidelines for objectives-based data collection • FMSP project R8462 – Guidance for developing data collection systems for co-managed fisheries (developed with Bangladesh DOF, to be published soon as an FAO FTP – see www.fmsp.org.uk) • Hoggarth et al (2005) – Specific data needs of FMSP Fish Stock Assessment Tools (Section 3.2) • Sparre & Venema (1998) – Survey designs for collection of LF data (Chapter 7) • Stamatopoulos (2002) – Simple guide to survey designs for C/E data

  6. References • Bazigos, G.P. (1974). The design of fisheries statistical surveys. Inland waters. FAO Fisheries Technical Paper, 133. • Caddy, J.F. and Bazigos, G.P. (1985). Practical guidelines for statistical monitoring of fisheries in manpower limited situations. FAO Fisheries Technical Paper, 257. • FAO. 1997. Fisheries management. FAO Technical Guidelines for Responsible Fisheries No. 4. Rome, FAO. 82 pp. • FAO. 1998. Guidelines for the routine collection of capture fishery data. Prepared at the FAO/DANIDA Expert Consultation. Bangkok, Thailand, 18-30 May 1998. FAO Fish. Tech. Pap. 382. Rome, FAO. 113 pp. • Hoggarth et al, 2005. Stock Assessment for Fishery Management – A Framework Guide to the use of the FMSP Fish Stock Assessment Tools. FAO Fish. Tech. Pap. 487. • Sparre, P. & Venema, S.C. 1998. Introduction to tropical fish stock assessment. Part 1. Manual (Rev. 2). FAO Fish. Tech. Pap. 306.1. Rome, FAO. 407 pp. • Stamatopoulos, C. 2002. Sample based fishery surveys. A technical handbook. FAO Fish. Tech. Pap. 425. Rome, FAO. 132 pp. http://www.fao.org/DOCREP/004/Y2790E/Y2790E00.HTM

  7. Data commonly used in Stock Assessment • Catch, effort and abundance • Use catch data to examine fishery ‘phase’, increasing or decreasing? (see next) • Use CPUE or other abundance estimate as index of stock size (explain why catches are increasing or decreasing), and as input to biomass dynamic models • Catch composition (Age frequency / length frequency) • Use to estimate (growth and) mortality rate as the indicator of fishing pressure • Intensive sampling also allows construction of a stock-recruitment relationship based on VPA methods (used to avoid recruitment overfishing) • Can also estimate size-selectivity of gears – important for setting mesh size rules • Age frequencies better if fish can be aged (see FTP 487 chapter 10) – gives better estimates of growth and mortality rates • Note catches may also need to be subdivided by species, in a multi-species or ecosystem management situation • Biological data • Analytical models also need inputs on size at maturity, fecundity at size, weight at length • Information on seasonality of spawning, feeding, growth and recruitment can also be useful for guiding the use of closed seasons in the fishery (see FTP 487 Sec 3.2)

  8. Use of catch data to show fishery ‘phase’ • Generalised Fishery Development Model • (see Grainger & Garcia, 1996; • and Hoggarth et al, Chapter 14) Grainger, R.J.R. & Garcia, S.M. 1996. Chronicles of marine fishery landings (1950-1994): Trend analysis and fisheries potential. FAO Fisheries Technical Paper. 359. Rome, FAO. 51 pp.

  9. Data needs of the different approaches / tools • Analytical approach (LFDA / Yield)(See FTP 487, Tables 4.1 & 4.3) • Catch composition data (either from length frequency data – LF, or ageing studies) • Biological data (e.g. size at maturity) • Management advice can be produced from just one seasons’ sampling (e.g. from a short time-series sample of LF and biological data) • But note some reference points also need long-term Stock-Recruit relationship • Biomass dynamic approach (CEDA) • Multi-year time series of catch and effort data, or catch data with a secondary index of abundance (e.g. from a survey) • ParFish approach • Uses C/E and/or abundance data as with the other biomass dynamic models • Due to Bayesian formulation, can also add other sources of information to improve the analysis, e.g. where few or no C/E data are available, and to ‘tune’ the outputs to local users preferences

  10. Data inputs of the FMSP analytical approaches (e.g. Yield) for estimating reference points • Use with LFDA or others to estimate growth parameters and F indicator • Note that different model options within the software require different inputs • Table 4.3 of FTP 487

  11. Data inputs of CEDA software • ‘No recruitment’ model for a short (e.g. 1 yr) time series; • DRP models for longer data sets (e.g. several years with good contrast) • FTP 487 Table 4.8

  12. Data needs for FMSP and other methods for estimating indicators Notes: Myr = multi-year, TS = time series of data, SS = single sample, Mat. = maturity at age data, Sel. = selectivity pattern by age Fishery phases: D = developing, M = mature, S = senescent (see Section 3.4.1). Subscripts: t = year, a = age, eq = equilibrium. FiSAT LBCA = length based cohort analysis ('pseudocohorts' method can also estimate Nt etc from time series data for fast growing species) Chapter 5, Table 5.1

  13. Data needs of FMSP reference point estimators Notes: Myr = multi-year, TS = time series of data, SS = single sample, Mat. = maturity at age data, Sel. = selectivity pattern by age or size DDRecr. = density dependence in SRR (Beverton & Holt 'steepness'), FMSY* = FMSY assuming constant recruitment R An. Var. = annual variability in recruitment (coefficient of variation) Chapter 5, Table 5.2

  14. General Guidance on Data Collection

  15. Collection of catch and effort data • First priority of data collection system • Usually either sampled at landing sites, or by completion of log books • Port samples raised to total catches within administrative and practical strata using frame survey and other data (see below) • May also use observers on board vessels, e.g. where problems with bycatches/discards may otherwise be unrecorded • May need to use General Linear Models (GLM) to standardize data for the fishery as a whole, where more than one gear type is used, or where fishing effort patterns have changed over time

  16. Estimation of stock abundance (from C/E or abundance survey data) • Needed as input for biomass dynamic models, which estimate the MSY of the fishery based on a time series of catch and abundance data • Can obtain index of abundance from CPUE of the commercial fishery data, • …. or from ‘fishery independent’ surveys using a standard survey track and gear type every year (e.g. ‘swept area’ surveys) • Commercial data may be easier to obtain, but may be biased by changes in catchability over time and by spatial factors in fishing patterns • Fishery independent surveys should be less biased but may be expensive (and may also be less accurate – i.e. have higher variance).

  17. Using effort units to ensure constant catchability, q • Biomass dynamic methods assume constant catchability, q, over the time period of the data set, i.e. that CPUE = q Biomass • Only works if effort, E is a good measure of the effect of fishing on the stock, i.e. that the fishing mortality rate is proportional to effort, F = q E • Effort measure may need to include number, power and size of vessels and gears, and the time actually fished (actively for some gears, or as soaktime for others) • e.g. for trawl fishery – boat-hours (for a standardized boat power and size) • See other examples on next slide for Bangladesh inland fishing gears (and see FAO, 1998 Annex 2 for general categories) • For multi-species, multi-gear fisheries, measuring effort is complicated, so sometimes use a standard fisherman-day or a fisherman-year as an effort measure. In this case, q clearly not constant, so take care.

  18. Examples of effort units for Bangladesh inland fishery • 1 Effort units including hours are either soakhours for unattended gears (e.g. traps, barriers) or active fishing hours for attended gears (e.g. push nets, spears) • 2 eg per katha/kua in Bangladesh. This effort measure takes no account of the different water areas fished.

  19. Length or age frequency data? • Age frequency data most useful where ageing is possible, as mortality rates are estimated directly and will be more accurate • With length-based methods, length samples are converted to age using a fitted growth curve, and then mortality rates are estimated, so accuracy is reduced by the extra step needed • Length-based methods needed for analytical methods for species which can’t be aged (e.g. crustacea, some tropical stocks) • But note alternative option to use biomass dynamic approach for these species also (using only C/E data, not catch compositions)

  20. Using length frequency data • Can estimate growth rates fairly easily from small samples (see LFDA tutorial) • … but good estimates of mortality rates need data on the total LF of the catch in the fishery, so you need to sample over the full year, and for different gear types and locations, and raise according to the relative catches etc (see Sparre and Venema, 1998). • Best results from length-based methods will be obtained by sampling gears that have small mesh sizes, and therefore give a wide size range of fish in the sample (may use special small-mesh sampling gears) • A growth curve can sometimes be fitted through a single sample, but a short time series of samples will increase confidence that a real curve has been detected (see e.g. in next slide) • Very difficult to use with gears such as gill nets as gear selectivity biases the samples too much • Be careful in the case of migratory stocks – see Sparre & Venema (1998) Chapter 11

  21. Example length frequency data (LFDA analysis)

  22. Collection of biological data • Used as inputs in analytical models (e.g. Yield, YPR) • E.g. maturity (by size and season), weight at length etc • Can be sampled every few years, unless conditions change significantly between years • May be incorporated into LF sampling, as a second step for some randomly selected fish … or as special surveys, depending on needs.

  23. Collection of interview data for ParFish • Stock assessment interviews • Use of other information as ‘priors’ • Preference interviews • See ParFish toolkit etc

  24. Data collection for empirical SA methods • ‘Empirical’ approach compares benefits of alternative management approaches as measured by indicators of the various fishery objectives • Useful where resources can be sub-divided into small units, with different management practices tried in each (e.g. in separate lakes, or co-management units in floodplain fisheries) • But can be confounded by other differences between units, so best to have large number of ‘sample’ units to compare • Need to measure both inputs to the system (management practices, other possible influences) and outputs from the system (catches, biodiversity, social benefits etc) • Analyse system influences using GLM or Bayesian Network approaches (see Hoggarth et al, 2005, Chapter 14, based on FMSP Project R7834, and guidelines of FMSP Project R8462)

  25. Data collection system design • Focus on need to provide information for management feedback on goals and objectives… (see next slide) • If goal is to maintain biodiversity, sampling must take multi-species approach • If management options include gear controls or restrictions, sampling must take multi-gear approach • If fishery is single-species, single gear, nice and simple! • Decide data collection needs, depending on which management measures and stock assessment tools are intended • See selection procedure in new guidebook • Design institutional arrangements depending on degree of co-management • Note need to involve collaborators in decision making (both on system design and management regulations), not just collecting data

  26. Examples of other data to collect (depending on goals, objectives and fishery type / situation) • Biological • e.g. discards by species and gear type • Ecological • impacts of fishing gears on habitats etc • Social • Numbers employed by fishery / fleet / gear • Numbers of processors, supply chain etc • Subdivision of stats by age group and gender • Economic • Fishers incomes, costs, profits (by gear / fleet type) • See FAO (1997, Tables 1-3) and FAO (1998) for detailed data to collect, and use of these data in formulating policy, making management plans and in monitoring performance.

  27. Template for selecting SA tools and data collection… (see new guidebooks from Project R8468) … Add more rows as necessary to cover all the operational objectives and tool options

  28. Data collection systems for different fisheries • Industrial and artisanal • Large scale fisheries vs small scale – differences in value affect management options and data collection needs • Freshwater and marine systems • Vary in degree of containment and controllability and in need for collaboration with ‘shared stocks’ (both with neighbouring countries at sea, and with neighbouring co-management units in river fisheries) • Most important to work on ‘unit stocks’ – may be more sub-divisible in inland fisheries than marine – but note ‘blackfish’ and ‘whitefish’ issues

  29. Sampling design • Applies to both C/E and LF data • Draw thematic map to show fishery areas, vessel harbours and landing sites, and potential sampling points (see next slide) • Need frame survey to give numbers of fishing units, both for sample design and to raise total catches (see data form in Iraq project doc?) • Estimate total catch by basic formula • CPUE estimated by landings survey – average CPUE by gear type, species etc • Four options for estimating effort given on next pages (see Stamatopoulos, 2002 for details)

  30. Example of thematic map • Caddy & Bazigos, 1985

  31. Frame survey • Inventory of fishing industry used to raise total catches from sample data • Repeat every few years (depending on rate of change) • Includes following data: • Size and area distribution of fishing sites (map) • Number of fishing boats (by type) • Number and type of fishers (full-time, part-time, migratory etc) • Number and type of fishing gears owned • (See Bazigos, 1974 for details)

  32. Estimating fishing effort – Option 1. • Complete enumeration (census) • Best result, but often impossible

  33. Estimating fishing effort – Option 2. • Census in space, sampling in time (Second best) • where: • AverE is the average fishing effort in boat-days over the sample days. • A is a raising factor expressing total number of days of fishing activities during the month, i.e. it is calculated each month.

  34. Estimating fishing effort – Option 3. • Census in time, sampling in space (third best) • where: • AverF is the average fishing effort exerted by a single fishing unit during the month and is associated only with the sampling locations from which data have been collected. • F is a raising factor expressing the total number of fishing units that are potentially operating at all fishing sites (i.e. the overall geographical stratum).

  35. Estimating fishing effort – Option 4. • Sampling in both space and time • (most common, economical for data collection, but least robust) • where: • BAC is the Boat Activity Coefficient, expressing the probability that any boat (= fishing unit) will be active (= fishing) on any day during the month. • F is a raising factor expressing the total number of fishing units that are potentially operating at all fishing sites (i.e. the overall geographical stratum as in Option 3). • A is a raising time factor expressing total number of days with fishing activities during the month (as in Option 2).

  36. Combination of surveys to estimate total catch • For Option 4. Sampling in both space and time • (See Stamatopoulos, 2002 for when to use Options 1, 2, 3 or 4)

  37. Survey stratification • Stratification is used to: • give more homogeneous target populations, which will provide lower variances in the estimates; • categorize the data population in order to respond to specific user needs (e.g. by different gear types, if different management regulations are being considered for each one); • allow reporting for different administrative, functional or other categories. • See FAO, 1998 Table 5.1 for examples of stratification variables: spatial, time, fishers/processors etc, vessel and gear types, social (communities, ages etc), environment (habitats), seasons…

  38. Data forms to use - Examples • Caddy & Bazigos (1985) – manpower limited situations • Bazigos (1974) – survey designs for inland waters • Stamatopoulos (2002) – simple examples for Options 1-4 above, and compatible with FAO ARTFISH database • Note need for standardization of data – e.g. using consistent codes for species names, and for allowing data exchange between adjacent states for shared stocks • See FAO, 1998 for general guidance on designing data sheets

  39. Database system • Need to keep good records, check for data entry errors and backups etc • Simple spreadsheets may be OK for small scale local units? • Need proper relational database for larger fisheries with strata etc • e.g. ARTFISH (download from FAO web site) • e.g. PISCES generic database produced by FMSP Project R7042 (www.fmsp.org.uk) • e.g. CARIFIS database? • Use/development of existing FRSS systems in Bangladesh?

  40. Discussion Questions • What data do you have already that could be used for stock assessment? In FRSS or in local management units? • How are catch and effort estimated? How reliable are the data? • What effort measures are used for different gear types? Would they provide useful, unbiased indicators of abundance? • Do you have any routine survey data that could provide abundance estimates (time series) • Do you collect length frequency or age frequency data? How often? For what species, what sample sizes? • Do you have biological data needed for analytical methods? • How could national (e.g. FRSS) and local management data collection systems be integrated, especially in inland fisheries? Are any data collected in both? • What other data should be collected, besides C/E, LF, biological?

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