Previous Page Table of Contents Next Page

3. What to collect and share


Section 2 described four broad categories of information required by managers to support the co-management process. This section provides more detailed guidance on the types of data required to generate this information and examines in more detail opportunities for information and data sharing.

The choice of data collected by co-managers will be influenced by a variety of factors. The primary influence will be the selected indicators used to evaluate performance in relation to various management and policy objectives, as well as any explanatory variables selected to explain the performance or outcome.


An indicator is a variable, pointer or index. Indicators are employed to evaluate the performance of management policies and plans implemented to meet various objectives or goals. Numerical indicators are typically calculated from data variables. Some important variables such as catch may themselves be used as (status or default) indicators. Some data variables are vital to a wide variety of indicators. Other more qualitative indicators may be assigned scores or values using subjective judgements. What data are collected is therefore largely dependent upon the objectives or management or policy, but other factors will also be important in deciding what to collect (see below).

Although measuring and monitoring indicators of outputs or outcomes is necessary, they cannot, by themselves inform managers whether or not the particular outcome can be improved or increased, or what measures could be taken to make improvements. For example, monitoring catch rates provides a means of monitoring abundance. If corresponding levels of fishing effort are also monitored, it should be possible to determine how effort should be managed to maximize or sustain yield and catch rates.

To reconcile this problem, inputs to the fishery or explanatory variables may also be monitored in order to explain the outputs or outcomes. These inputs and outputs may be combined to form models. These models may be informal for example cognized (conceptual) models of the fishery developed through perception, reasoning, intuition, or even superstition. More formal models include empirical models developed on the basis of experience or adaptive management (see Section 0); and analytical models of the fishery (see Section 3.5.6) with associated target or limit reference points (see Caddy and Mahon, 1995). More holistic frameworks such as the Sustainable Livelihoods Framework (see Section can be used to help understand the core influences and processes determining livelihood outcomes. These frameworks and models may be expressed verbally, graphically, physically or quantitatively. The choice of model employed will therefore also have an important bearing on the type of data that is collected.

The choice of model or framework will depend largely upon management objectives and institutional capacity. Local communities are more likely to employ informal cognized models or maps to plan their activities and manage their resources (see, whilst fishery departments are more likely to have the capacity to collect, collate and analyse data and information to monitor status indicators, build empirical or analytical models or to employ more holistic frameworks such as the SL framework2.

3.1.2 Other factors influencing the choice of indicators, and data variables

These have already been described by FAO (1999), and therefore we only briefly mention them here so that readers are aware of their potential influence over the choice of indicators and data variables:


Policy decisions are best made in the macro-policy and macro-economic (multisectoral) context. It is therefore important that policy and planning decisions are made in the full knowledge of the role of fisheries in the regional, national and local economy, and the implications, costs, benefits and alternatives for use of the resources, before the best policy decisions can be made (Section 3.2.1).

The Importance of policy information
“[In Lao People's Democratic Republic] The strengthening of the national fishery statistical systems as an integral part of a planning and decision‑making process should be a major national fisheries objective in the drive towards sustainable fisheries and food security…Official figures do have a major influence on national policies …donor perceptions and therefore their investment strategies. ” (Hartmann, 2004).

Fisheries policy often reflects national legislation, the broad development and poverty reduction goals of governments as well as obligations resulting from international development agreements, or ratifications of conventions, codes of conduct or voluntary instruments which define various management and (regional) reporting obligations. The most important of these in terms of shaping policy are described below together with their associated data requirements. Other obligations, particular with respect to the provision of data and information are described in Section 3.2.4 including CITES, RAMSAR, Convention on Biological Diversity, etc. Co-management policy itself is also often subject to evaluation. This evaluation process will demand its own suite of data and information (Section 3.2.2).

To be consistent with existing FAO literature, we have adopted a similar format to FAO (1999) to present examples of typical data types and variables.


The significance of fisheries with respect to the regional, national and local economy must be understood before the best policy decisions are made in relation to other sectors of the economy. This demands a clear understanding of the position or status of the fishing in the national socio-economy. Policy and development planning decision-making therefore requires information relating to the importance of fisheries in terms of economics, employment and food production, and sometimes in terms of recreational opportunities. Information relating to the costs generated by the fisheries, in particular monitoring, control and surveillance, subsidies and the opportunity cost of the fishery in relation to competing sectors, is also required (FAO, 1997).

A number of key macroeconomic indicators used to guide policy and development planning decisions include: Gross value of production (GVP)

The gross value of production is the product of total production and the price received and provides an indication of the potential economic importance of the fishery relative to other fisheries or industries in a nation, region, province or district. It should include data from both the co-managed and non-co-managed sectors. Estimates of GVP may be required by other relevant government departments to estimate the contribution fisheries makes to the national GDP.

Variables and sources

Primarily from the harvest sector and local, national and regional markets. Opportunities may exist to obtain production estimates from fish consumption data obtained from population census exercises undertaken by other ministries or statistical bureaus, combined with trade data (imports and exports). International price data are also available from various sources such as Globefish (

Examples of GVP variables

Data typeData variables
ProductionLanded weight of species from co-and non-co-managed sectors
Unit PricesUnit price of species Food supply and fish consumption

Fish is a major source of animal protein to people in the developing world. Fish supply and trends in average per capita consumption provides an indication of dependence on fish as a food source at different administrative levels. This information is useful when formulating policies on trade and monitoring food security. Significant trends in per capita fish consumption and fish consumption as a proportion of total protein consumption can be indicative of the ability of fisheries performance in meeting the primary objective of human nutrition.

Total national food supply (tonnes/year) is a product of total domestic production and fish imports minus exports. Fish consumption can be expressed as kg/capita/year but does not provide an indicator of distribution within the population. Ideally, a Gini coefficient should be calculated for fish consumption - that is, the deviation between observed cumulative consumption as described by a Lorenz curve and the cumulative consumption expected from equal distribution (see Section

Average fish consumption per capita may be estimated from the total annual national consumption (AFC) divided by the estimated total population (Npop) where:

AFC (kg y-1) = annual domestic fish production + (annual fish imports minus annual fish exports)

Annual domestic fish production is the sum of the total annual catches all food fish species. The term “food fish” here is taken to represent all catch and cultured products excluding mammals and aquatic plants (FAO, 1999).

Variables and sources

Data originate from the harvesting, processing and marketing sectors. Import and export data and are available from the relevant trade ministry records and population and consumption data fish consumption data may be available from population census exercises undertaken by other ministries or statistical bureaus.

Examples of per capita food supply variables

Data typeData variables
LandingsQuantity of fish landed from co-and non-co-managed sectors
Fishery imports and exportsQuantity of fish products imported and exported
Conversion factorsRatio of weight of product to weight of protein by product or species
National populationNumbers of people; fish consumption; average food consumption by food type Employment in the fisheries sector

Artisanal fisheries within the developing world often provide livelihoods for the most vulnerable groups within society. The opportunity cost of fishing may be near zero and displaced or landless groups may use the fishery as a supplementary or last resort source of income and nutrition. Information regarding changes in the total number of people employed in the sector overtime (on a seasonal basis and across sub-sectors) would provide a useful indicator of the value of the fishery to local communities. The number of people employed in the fishery can also provide information on the importance of fisheries and related activities to the regional and national economy.

Variables and sources

There are few examples of reliable statistics regarding fisheries employment in the artisanal sector. Ideally, this information should be generated through routine national census or statistical collection and reporting systems, or failing this through periodic frame (Section or ad hoc survey exercises (Seki and Bonzon, 1993). Estimating employment is complicated by the diversity and seasonality of economic activities within artisanal fishing communities but classification of fishers could follow the FAO Fisheries Information, Data, and Statistics Service (FIDI) categorization of “full-time”, “part-time” and “occasional fishers”.3

Information on secondary employment such as trading and processing is less likely to be available. Estimates of secondary employment can be made with fixed conversion factors suitable for the fishery and the surrounding economy in question. Seki and Bonzon (1993) recommend separate conversion factors for African inland and marine fisheries (inland fishers × 5, and marine fishers × 3). Similarly, if each fisher is assumed to support 4 dependents on average an estimate of the total population directly or indirectly dependent on the fishery can be made. Balance of trade and foreign exchange earnings

The balance of trade and foreign exchange earnings may be other important indicators of the importance of fisheries to the national and regional economy. See FAO 1999 for details of typical variables and sources.

Examples of employment variables

Data typeData variables
Number of persons employed in fisheryEmployees by primary, secondary and tertiary sectors and by category e.g. full-time, part-time, and occasional in both co-and non-co-managed sectors
Employment in non-fisheries sectorEmployees
UnemploymentUnemployment nationally, by region, district
Conversion factorsNumbers of employees in secondary and tertiary sectors per fisher Community dependence

Community dependence on fisheries is usually expressed in terms of percentage dependence on fish for food, protein and income. Indicators might include percentage of total income derived from fishing, or percentage of total protein consumed derived from fish. Variables will include demographic variables of interest (income group, region, age, etc.) and indicators of food security (see above) and income (see below).

3.2.2 Data to evaluate co-management policy performance

Data to evaluate co-management policy performance will depend upon the over-arching fisheries policy objectives of the state and the selected indicators used to monitor performance against these objectives. Evaluating co-management policy may require the monitoring of selected performance indicators through time, often in relation to targets or compared against equivalent indicators monitored within the non-co-managed sector. Performance indicators may be averaged across co-management units or fisheries, or summarized in appropriate tabulations, frequency distributions or other graphical summaries. Progress towards establishing co-managed fisheries

Indicators may include the number of co-management units established, numbers of fishers participating in co-managed fisheries, the proportion of landings taken by the co-managed sector.

Variables and sources

Management plans should provide a source of data relating to the number of co-managed fisheries as well as the numbers of fishers involved (Section 3.3). Landings data might be generated by monitoring programmes aimed at monitoring resource sustainability (see Section or for evaluating the performance of (local) management plans (see Section3.5).

Examples of data types and variables used to monitor progress towards establishing co-managed fisheries

Data typeData variables
Number of co-managed fisheriesCo-managed fisheries by region, province, marine/inland
Number of fishers participating in co-managed fisheriesNumbers of fishers by income group
LandingsQuantity by sector (co-and non-co-managed sectors) Conservation and resource sustainability

Since the achievement of most policy objectives depends upon the sustainability of fish stocks, monitoring their ecological state, particularly in terms of their abundance will always be necessary.

Monitoring absolute abundance of fish stocks using biomass survey methods is unrealistic in most cases. More commonly, ‘catch per unit effort’ (CPUE) is monitored as an index of stock (see Section 3.5.1). Maintaining levels of CPUE that both safeguard the future of the stock as well providing high levels of yield is a fundamental goal of management. Monitoring the relative values of CPUE among species present in the fishery over time can also be used to monitor the effect of fishing on species diversity and ecosystem integrity. Simply monitoring the number of species landed by the fishery (species richness) could provide a simple alternative to these diversity indices.

The effectiveness of policy in respect to conservation and resource sustainability goals may thus be judged in terms of trends in CPUE and species diversity among co-managed fisheries or sites compared to the conventionally managed sector.

Examples of data types and variables used to monitor conservation and resource sustainability

Data typeData variables
IdentifiersCo-managed fishery name or ID; management area name, LMI identifiers, region, strata, etc.
Abundance indicesMonthly CPUE by species for a standard unit of effort
Biodiversity indicatorsNumber and names of species landed Compliance with rules and regulations

Changes in compliance, or comparisons of compliance with rules and regulations among co-managed fisheries may provide insights into the effectiveness of co-management policy, particularly with respect to the institutional and decision-making arrangements, enforcement measures, as well as the appropriateness of the selected rules and regulations governing access and fishing operations. Interdisciplinary explanatory variables that might be monitored to explain differences or trends in compliance, as well as the other co-management policy and management plan performance indicators are discussed in Section 3.5 below.

Variables and sources

Indicators of compliance should provide measures of the number and type of non-compliance activity and might include the average number of unlicensed boats fishing during a day for a given month; the proportion of fishers employing illegal gear types; or the quantity of fish landed during a closed season. Explanatory variables might include the details of resources devoted for enforcement, and details of sanctions for non-compliance. Data sources include relevant administrative levels of the fisheries department, and the records maintained by the LMI.

Examples of variables for monitoring and understanding non-compliance

Data typeData variables
IdentifiersCo-managed fishery name; management area name, LMI identifiers, region, strata, etc.
Non-complianceNumber and type
Sanctions for non-complianceWarning, confiscation of gear/vessel/catch; revocation of licence, fine
Other explanatory variablesNumber of guards per unit area; clearly defined boundaries; representation in rule making, legitimacy of decision-making body; local support of co-management arrangements; knowledge of rules and regulations; expenditure on enforcement by local district officers Food security

Food security is likely to be an important indicator of co-management performance, particularly in respect to how it varies among different socio-economic groups. Therefore in addition to the variables identified in Section, it would also be necessary to collect demographic variables of interest such as age, ethnicity, income group, region etc. Fish consumption data may also be collected using dedicated household surveys or part of national census exercises. The use of simple indicators such as the “number of days per week or month without fish meals” is common among routine monitoring programmes.

Examples of food security variables

Data typeData variables
LandingsQuantity of fish landed
Fishery imports and exportsQuantity of fish products bought and sold
Conversion factorsRatio of weight of product to weight of protein by product or species
Population/HouseholdNumbers of people; fish consumption indicators; average food consumption by food type; demographic variables (age, gender, ethnicity, etc.) Income

Income is an important micro-economic indicator of management performance, normally assessed at the (local) management plan level and therefore like CPUE, may be of significant interest to the LMI. However, changes to fisher income (and its distribution - see later) may also be of interest at the national level to evaluate performance of co-management policy. This performance may be judged in terms of trends in fisher income or relative levels of income among co-managed fisheries compared to the conventionally managed sector. Income is typically evaluated on the basis of costs and earnings data (Halls et al., 2000).

Variables and sources

Costs are treated as fixed costs or variable costs. Fixed costs are considered as expenditure related to capital (such as investments in gear and vessel) and may be independent of the level of output. Variable costs are continuous expenditure relating to everyday running costs (including fuel, repair, ice, food and crew costs etc). Variable costs would usually include some payment for the right of access to the resource. These costs may include traditional taxes or offerings collected for church/temple/village funds and utilized for social and religious purposes or those funds paid to leaseholders and other formal or informal owners or intermediaries.

Examples of income variables

Data typeData variables
Fixed costsGear, vessel investment; insurance; depreciation
Variable costs (owner operating)Repair and maintenance of craft; repair and maintenance of gear; food; materials; stocking costs
Variable costs (common operating costs)Food; traditional taxes and offerings; materials; commission; repair of craft and gear; remuneration to other owners; repayment of loans; stocking costs
EarningsFresh fish sales; processed fish sales; sales of fishing inputs; rental of gear; sale of fishing rights; investment

Cost and earnings data are collected using cost and earning surveys (CES), applied either to the FEU (fisherman/gear/vessel) combination operating from primary sampling units (PSUs) e.g. landing sites, or directly to PSUs in the case of household surveys where the PSU is also the FEU.

Caddy and Bazigos (1985) recommend stratified two-stage sampling with structured interview methods using pre-designed survey forms where FEUs or PSUs are sub-sampled from those selected for the catch assessment survey, if applicable. This “integration principle” improves efficiency, reduces the overall data collection costs and improves the utility of the results obtained. Before any selection is made, the sample units are stratified according to various strata, for example, region, fishery, socio-economic groups, fishing gear/vessel type (sub-sector), investment by unit of gear, etc. A few sampling units are then selected, with equal probabilities, from each strata of interest. Stratifying in this way also allows the calculation of Gini coefficients of income distribution among categories of interest (see below).

Most cost and earnings survey forms are detailed. Targeting the same model households between surveys is preferable as data quality and recall by respondents is likely to be higher and the process of scaling up is simplified (Poate and Daplyn 1990). Such panel survey methodologies are regularly deployed to monitor long-term trends in income (see Dercon and Krishnan, 1998). Ideally, cost and earnings surveys would incorporate all flows into and out of the fishing economic unit (FEU) under scrutiny (fishing unit owner, household, community, etc.).

Changing investment levels is a good proxy indicator of changing economic performance and output (FAO, 1999). Investment can involve the acquisition of greater capacity through additional fishing units or improvements in efficiency of existing fishing units. Relevant data include number of licensed vessels by vessel class and sales recorded by secondary support sectors such as gear-repairers and sellers.

Other proxy indicators of socio-economic status might be utilized if these are designed in preparatory phases of the monitoring programme. Realistic checklists for information requirements can only be established and refined through these preparatory phases and interview or survey strategies must adopt suitable protocol for the sampling of sensitive information. Caddy and Bazigos (1985) recommend the survey of simple proxy indicators of economic well-being e.g. “are incomes high enough to allow fishers, to repair or purchase boats and gears?”, “are sources of credit readily available?” Poate and Daplyn (1990) question the reliability of cost and earnings surveys within the agricultural sector and suggest the adoption of suitable proxy:

“…. it is prudent for the survey designer to question the wisdom of even trying to collect income, expenditure and consumption data, before embarking on design and exploratory surveys. Unless very high standards of enquiry are achieved the results are likely to be unreliable, and potentially damaging if the users of the data are not aware of their shortcomings. An alternative approach is to avoid the problem of measuring total income or expenditure by concentrating on physical production, which can then be modelled using price and marketing data. Proxy measures of wealth, and access to or participation in social activities such as education, may convey sufficient information about economic well-being. If a survey is unavoidable, we suggest that a small (case) study of a few households under good supervision will provide more reliable and usable data than a large-scale sample survey. Expenditure data are likely to prove more reliable than income data.” (Poate and Daplyn, 1990) Distribution of income/consumption/benefits

The Gini coefficient (G) is a useful means by which to quantify the distribution or equity of benefits, such as income and nutrition among individuals or groups or categories of individuals:

where y1….nyn represent incomes or annual fish consumption of individuals of each group or category in decreasing order of size, is the mean income or annual fish consumption of all the groups or categories combined, and n is the number of socio-economic groups or categories under examination.

Distributional equity may be quantified in terms of the deviation in the observed value for G from the expected or desired value (Lorenz 1905). Sen (1976) combined the three aspects of head count, average shortfall from the poverty line, and inequality into a comprehensive and commonly used poverty index:

S = H[I + (1 - I)Gp

where H is the poverty headcount ratio, I is the average income or fish consumption shortfall of the poor in percentage terms, and Gp is the Gini coefficient of income or fish consumption inequality among the poor.

The calculation of Gini coefficient for income distribution requires fisher household cost and earnings data monitored by panel survey methods (iterative sampling of identifiable model households). Calculation of the Sen poverty index (S) would rely on an identical set of household data.

The distribution of wealth and income form the fishery is likely to be closely linked to access arrangements (see below). This is especially true in heavily exploited fisheries, where the expansion of fishing effort by one group is likely to impact negatively on other groups.

Calculation of the Gini Coefficient (G) to quantify the distribution of nutritional benefits would require detailed information of diet for as many households or groups as possible but stratification according to sub-sector or management unit of interest, and with reference to an appropriate proxy such as fish meals/week, could more realistically be sampled.

In this instance y1….nyn represent individual, group or category annual fish consumption in decreasing order of magnitude; y is the mean individual fish consumption across all individuals, groups or categories; and n is the number of individuals, groups or categories.

To determine the distribution of nutritional benefits from fisheries a panel survey equivalent to that for income should be designed. Representative households must be sampled iteratively to record “number of fish meals” consumed annually and number of dependants ( y and n, respectively). Poverty

Indicators of poverty have typically been macro-economic statistics regarding growth, investment, balance of payments...etc, but these have failed to represent distributional aspects of development. Fields (1994) defines poverty as: “…the inability of an individual or a family to command sufficient resources to satisfy basic needs.”

The poverty line is the reference point by which to gauge development and is defined by standards set by that country and according to its particular stage in economic development. Once the reference point is set, the extent of poverty can be gauged by the shortfall between desired and actual income. In acknowledging that the costs of living may differ between regions, some countries have set separate rural and urban poverty lines (e.g. India and Costa Rica).

Fields (1994) suggests the sampling of larger economic units - that is, sampling of households as opposed to the individual. The household unit quickly encompasses more individuals and accounts for the sharing of family income. The frequency of sampling is critical. Long reference periods are more appropriate for capturing long-term trends but data quality suffers from long recall periods. Ideally, sampling would occur on a monthly basis. Poverty lines have been constructed as some fraction of average wage (as in Brazil) but this overlooks access to basic needs and commodities. The most common way to set reference points is to estimate the cost of a basic food basket (the cost of nutritional necessities as defined by calorific and protein content). Most developing nations have established poverty lines according to this type of criteria and will be unique form country to country.

With regards to quantifying the attainment of these reference points the simplest measure is an income head count in relation to this level of poverty. This does not, however, provide information on the distribution of poverty or, in fact, to what degree sections of society are poor. The generation of this level of information requires data on incomes by strata of interest. Ideally, data requirements for poverty evaluation would be derived from household income surveys (see above) conducted on a national scale. Alternatively it may be possible to employ a case study approach (see above) or obtain levels refined measures of income from a national census (Fields, 1994).

Following the work of Amartya Sen and the emphasis on poverty as lacking access to social capital or entitlements, there has been a re-appraisal of the financial treatment of poverty. The sustainable livelihoods (SL) approach adopted by DFID (see Section acknowledges the complexity of the poverty issue. Ideally, a checklist analogous to the sustainable livelihoods approach would be adopted where human, social, natural, physical and financial capital are monitored but recognized as inter-dependent. The problem here, however, is to understand the processes by which these attributes influence one another and the problem of capturing the essence of abstract concepts such as “social capital” (see Serra, 1999). Access to (or exclusion from) basic infrastructure and services provides alternative poverty indicators. Hundreds of indicators have been developed and applied such as “distance to doctor”, “distance to clean water”, “proportion of children in primary education” etc. As with the design of poverty lines, such indicators can be global but are more suitably developed nationally or on a regional basis (Halls et al., 2000).

Variables and sources

Variables include cost and earnings data and relevant demographic variables of interest. Numerous proxy indicators may be substituted for income data such as gear/vessel ownership, savings, investments, assets, access to services and credit, material possessions, household assets, etc. Proxy indicators are usually collected infrequently (once every 1–10 years) as part of frame/ socio-economic baseline or may be available from population census data. Indicators of poverty, including guidelines for their data collection are further described below in Section 3.2.3. Access to resources

Access to resources will depend upon national co-management policy as well as local institutional and decision-making arrangements.

Variables and sources

Details of access rights and the basis with which they are governed and regulated should be explicitly defined in the local management plan (see Section 3.3). Data sources are mainly from the LMI, intermediaries and the government fisheries agency itself.

Examples of access variables

Data typeData variables
IdentifiersCo-managed fishery name; management area name, LMI identifiers, region, strata…etc
Access rightsNature of access granted to stakeholders (e.g. open, reciprocal, restricted, etc.)
Institutional and decision-making arrangementsRules for membership, and procedures for making decisions both formal and informal that govern access to and use of the resource based upon demographic characteristics, (e.g. gender, age, income group etc) or community of residence. Conflict

Conflicts can occur between the whole range of stakeholders, at a range of geographical levels and manifest themselves in a variety of ways. Although conflict is not an exclusively modern characteristic of fisheries, its study and quantification in this context has only recently been attempted (Neiland and Bennett, 1999). The DFID-funded project ”Management of conflict in tropical fisheries” (R7334) developed a typology of conflict which may help document change in the nature or severity of conflict within the fishery sector. The project also developed methods to identify conflict and its frequency of occurrence.

The characteristics of conflict between fisheries will differ according to setting. Which conflicts are seen as key and particularly disruptive by government and community may also be unique. However, disputes tend to focus on issues of access and exclusion (e.g.. ethnicity, in the case of Muslim and Hindu river fishers in Bangladesh and, in the Turks and Caicos Islands, access rights granted to foreign fishers). Where conflicts such as these are persistently disruptive it should be possible to record the incidence of disputes. Sometimes, an arbitration process might be formalized and institutionalized (as is the case with Ghana's Community-Based Fisheries Management Committees), and process documentation in the form of minutes must be made available for all cases heard by the committee or mediating body concerned. Where such a process has not been formalised, sources of conflict data may have to be improvised. In the Turks and Caicos Islands, the Fisheries Advisory Committee is required to document grievances and disputes identified by fishers within Fishery Management Plans drawn up for each fishery (Halls et al., 2000).

Variables and sources

Where ad hoc monitoring programmes are devised in relation to ongoing development projects, information is often collected regarding conflict. Impact monitoring is designed to record if conflicts have increased, decreased or, in fact, been introduced by programme activities themselves. For instance, within the WorldFish Center Community-Based Fisheries Management Project in Bangladesh, historic records of ongoing disputes and dialogue are recorded in the minutes of Local Management Committee meetings.

If this process documentation needs to be reduced further to simplify the process of data collection, then incidence of conflicts by type could provide simple indicators (see table below).

The incidence of each conflict should ideally, be determined on a seasonal basis since movements of fisher groups into and out of the fishery may follow seasonal patterns and dictate the nature of fisher-fisher interaction. Conflict data may be available from NGO facilitated community group/project records and minutes, or from local court records. Alternatively, the data could be collected with ad hoc studies employing semi-structured interview techniques with representatives of the LMI or other local stakeholders.

Examples of conflict variables

Data typeData variables
IdentifiersCo-managed fishery name; management area name, LMI identifiers, region, strata, etc.
Incidence of conflicts Number of conflicts or conflict events by type e.g. verbal confrontation; physical confrontation; injuries or deaths; incidents of gear damage; incidents of vessel damage; legal / tribunal cases (including both formal and informal / traditional village courts).
Reasons/explanationsReasons/explanations for dispute or conflict and resolutions Co-management costs

Costs are an important measure of co-management policy performance, particularly when compared against the benefits (e.g. improved income, equity, food security, etc.- see above) arising from the implementation of the policy. Contrary to popular belief, the costs of co-managing a fishery may exceed those for more conventionally managed fisheries, particularly if the state continues to have a significant role in monitoring and enforcement activities. Initial costs may be met through donor projects or programmes. Long run costs might be met from access or licensing activities.

Variables and sources

The co-management costs will include all those required to fund the various roles adopted by the government and local stakeholders (see Table 1). Categories of costs include including administration, monitoring, research, evaluation, enforcement and opportunity costs incurred by local stakeholders (see Section which discusses the importance of opportunity costs in relation to the design of data collection systems).

The primary sources are the administrative levels of government, the LMI and local stakeholders.

Examples of co-management cost variables

Data typeData variables
Costs to governmentSurveillance costs, monitoring costs, enforcement costs, training costs, administration costs, research costs.
Costs to the LMI and its associated stakeholdersOpportunity costs associated with participation in co-management activities (monitoring and enforcement activities, participating in meetings and workshops, and participatory monitoring programmes) Explanatory variables for co-management policy performance evaluation

Since broad co-management policies are likely to be translated into more context-specific local institutional and decision-making arrangements and management strategies, which may themselves differ significantly among individual co-management units or fisheries, developing an understanding of the effects of co-management policy on performance may only be achievable through local level comparisons. Opportunities exist to make these comparisons and build understanding as part of the evaluation of local management plan performance. Section 3.5.8 provides examples of the types of explanatory variables that might be used for this purpose in relation to the co-management policy performance variables and indicators described above. This section also contains guidelines for constructing predictive models. Process monitoring

The indicators described in this Section 3.2.2 have so far been relevant for monitoring the outcomes of co-management policy. However, it is also important to monitor the policy itself and how it is implemented in order to understand and improve the policy outcomes. The influence and outcomes of co-management policy will be reflected in local management plans as well as records/diaries documenting the outcomes of meetings and workshops held between the co-managers. Intermediary organizations such as research institutions, development projects and NGOs are often in a good position to take responsibility for this process monitoring. “Process monitoring should provide a means of developing stakeholders' capacity for participation and not as a means for allocating blame for management failure” (Hoggarth et al., 1999).

3.2.3 Data requirements for development and poverty reduction evaluation

The extent to which fisheries departments will have involvement in the monitoring and evaluation of poverty reduction strategies and development activities will vary. Most indicators used for monitoring progress towards poverty reduction and development are of cross-sector relevance. Their inclusion in fisheries sector data collection systems will therefore depend largely upon the degree of livelihood dependence on fisheries and the roles and responsibilities of the management authority. The fisheries sector may be involved in their collection and monitoring to contribute towards national efforts or to provide evidence of the effects of fisheries sector policy or interventions on achieving these goals.

Data required to monitor several of the proposed or recommended indicators, may often already be collected for monitoring the performance of co-management policy on poverty, conservation and sustainability (see Section 3.2.2). For example, data on the numbers of fishers below the poverty line could be used to help compile “percentage of the population living below the poverty line” for National Strategies for Sustainable Development (NSSD) purposes (See Section Annual catch by species is vital for many indicators and can provide the indicator for theme 17 of NSSD. Similarly, data relating to areas or reserves that have been set-aside for the purposes of maintaining or conserving fish diversity as part of a local management plan could be used to help compile the Millennium Development Goal (MDG) indicator: “ratio of land protected to maintain biological diversity to surface area” (see Section

Therefore, before establishing new (fisheries sector-based) data collection systems specifically to monitor progress with respect to Poverty Reduction Strategy Papers (PRSP), MDG and NSSD, it is worthwhile first reviewing, with respect to each indicator, currently available data collected to monitor the performance of co-management policy and (local) management plans (see Section 3.5 and 5.2.4). This being said, it may not be possible or appropriate to compile these indicators on the basis of separate contributions from different sectors such as fisheries. Instead ad hoc surveys or a regular census may be preferred, conducted by the relevant government department or line Ministry, such as a national statistical office, statistical bureaus or administrations, and possibly funded by donors such as the World Bank, the US Agency for International Development and the UK Department for International Development.

For any of the indicators described below, there may be a wide range of data sources available within the country, and whilst each source should be critically reviewed, existing data sources and reporting systems should be used where possible, particularly where line ministries have their own statistical systems. For example, the fisheries management authority may have relevant data relating to the areas or reserves set aside for the purposes of maintaining or conserving fish diversity which will be required to help compile the “ratio of land protected to maintain biological diversity to surface area” where the surface area corresponds to that of the State and its territorial waters (up to 12 nautical miles). Millennium Development Goals

In September 2002, 189 countries, including 147 Heads of State adopted the United Nations Millennium Declaration, which sets out a number of international development goals that have come to be known as the Millennium Development Goals (MDG). The aim of these goals is “to create an environment - at the national and global levels alike - which is conducive to development and the elimination of poverty.” By the year 2015, all 191 United Nations Member States have pledged to meet the MDG. The eight goals were chosen to monitor progress at the global level and guide development assistance; they are not meant to determine which goals individual countries should choose.

Forty-eight indicators have been identified to monitor progress towards these goals and targets. Full details of each indicator including rationale, method of computation, gender issues and guidelines for collecting data to compile the indicators are available at: Poverty Reduction Strategies

Since 1999, Poverty Reduction Strategy Papers (PRSP) provide the basis for assistance from the World Bank and the International Monetary Fund (IMF) Fund as well as debt relief under the Heavily Indented Poor Countries (HIPC) initiative. Developing or strengthening a poverty reduction strategy is on the agenda of about 70 low-income countries as a requirement for receiving debt relief under the enhanced HIPC Initiative and concessional assistance from the World Bank and International Monetary Fund (IMF). In effect, PRSP translate the World Bank's Comprehensive Development Framework (CDF) ( principles into practical plans for action (Box 6). These PRSPs fundamentally shape policy both within and across sectors and therefore are also likely to have a significant bearing on the design of [fisheries co-management] data collection and sharing systems.

There are five core (CDF) principles underlying the development and implementation of poverty reduction strategies. The strategies should be:

There is no blueprint for building a country's poverty reduction strategy. Rather, the process should reflect a country's individual circumstances and characteristics. Nevertheless, the core principles underlying the PRSP approach suggest that PRSPs should have:

The Comprehensive Development Framework
The Comprehensive Development Framework is an approach by which countries can achieve more effective poverty reduction. It emphasises the interdependence of all elements of development - social, structural, human, governance, environmental, economic, and financial. It advocates: a holistic long-term strategy; the country in the lead, both “owning” and directing the development agenda, with the Bank and other partners each defining their support in their respective business plans; stronger partnerships among governments, donors, civil society, the private sector, and other development stakeholders in implementing the country strategy; and a transparent focus on development results to ensure better practical success in reducing poverty.

A Joint Staff Assessment (JSA) evaluates the soundness of each PRSP in terms of whether or not the strategy presented constitutes a sound basis for concessional assistance and debt relief from the IFIs. The CDF and the PRSP are the way forward to enhance country ownership and the achievement of the Millennium Development Goals.

Data and information requirements in support of PRSPs are discussed in detail in the “Sourcebook”. The Sourcebook has been compiled to provide guidance and analytical tools to countries and country teams developing poverty reduction strategies. It is a collection of broad policy guidelines, examples of international best practice, and technical notes covering data and information requirements and monitoring and evaluation programmes. The Sourcebook is available on the Web, free of cost, at, and further updates may be found at that address. The Sourcebook was also published in bound form in two volumes in October 2001 (e-mail Participatory approaches to monitoring poverty are described at National Strategies for Sustainable Development (NSSD)

The United Nations Conference on Environment and Development (UNCED) held in Rio de Janeiro in 1992, recognized the pressing environment and development problems of the world and, through adoption of Agenda 21, produced a global programme of action for sustainable development into the 21st century. Agenda 21 states that countries should adopt national strategies for sustainable development (NSSD), which “should build upon and harmonies the various sectoral economic, social and environmental policies and plans that are operating in the country” (Dalal-Clayton and Bass, 2002).

Since UNCED, governments have made extensive efforts to integrate environmental, economic and social objectives into decision-making by either elaborating new policies and strategies for sustainable development, or by adapting existing policies and plans. To assist in this process, an International Forum on National Sustainable Development Strategies was held in Ghana in November 2001. The Forum adopted a guidance document containing a number of recommendations on approaches for integrating the principles of sustainable development into policies and programmes of both developed and developing countries (ibid).

The World Summit for Sustainable Development (WSSD), held in August 2002, urged that: “States should: Take immediate steps to make progress in the formulation and elaboration of national strategies for sustainable development and begin their implementation by 2005”.

Data and information requirements for NSSD

Indicators for monitoring progress towards sustainable development are needed in order to assist decision-makers and policy-makers at all levels and to increase focus on sustainable development. The Commission on Sustainable Development (CSD) has developed a set of 58 indicators (and accompanying methodology sheets) from which countries can choose from according to national priorities, problems and targets. See

Further advice and information concerning the formulation, implementation and monitoring of National Strategies for Sustainable Development can be found in the Resource Book at - contents Rationalizing poverty and development indicators

The types of data and information required to monitor development and poverty reduction performance at the national level for PRSP and NSSD, and globally in respect to the MDG are likely to have much in common. Indeed, many of the indicators for monitoring progress towards achieving the MDG have been recommended for monitoring progress towards reducing poverty as part of PRSP. With DFID support PARIS21 - a task team to consider how the international statistical community can improve their support for monitoring progress towards development goals - is currently examining ways in which monitoring efforts in respect to MDG and PRSP could be rationalized, as well as identifying the key constraints to improving data availability and quality (see

3.2.4 Data to meet management and reporting obligations

Fisheries policy is often shaped and influenced by obligations resulting from international development agreements, or ratifications of conventions, codes of conduct or voluntary instruments that define various management and reporting obligations. Conventions and Codes of Conduct

Chief amongst the international instruments is the United Nations Convention on the Law of the Sea (UNCLOS III). This convention sets the legal context for all subsequent international arrangements and agreements relating to the use of the oceans and seas (Cochrane, 2002).

The FAO Code of Conduct for Responsible Fisheries (CCRF) is a voluntary agreement which sets out principles and international standards of behaviour for responsible practices with a view to ensuring effective conservation, management and development of living aquatic resources, with due respect for the ecosystem and biodiversity. This includes the precautionary approach to fisheries management that requires managers to be cautious when the state of the resource is uncertain, for example when fishery data are insufficient or unreliable. The precautionary approach is thus a powerful incentive for the collection of reliable and relevant fisheries data (FAO, 1999). The code also emphasizes the importance of participation and contains provisions to protect small-scale fishers' livelihoods from conflict with larger-scale commercial interests, as well as providing the necessary framework for maintaining or enlarging small-scale fisherfolks “action space”. It also supports the role of community in bringing about development and resource conservation. Paragraph 7.1.2 of the Code of Conduct emphasizes the importance of involving legitimate interested parties in the management process (Cochrane, 2002), including the use of traditional knowledge:

Code of Conduct for Responsible Fisheries (CCRF)
“Conservation and management decisions for fisheries should be based on the best scientific evidence available, also taking into account traditional knowledge of the resources and their habitat, as well as environmental, economic and social factors. States should assign priority to undertake research and data collection to improve knowledge of fisheries” (CCRF 6.4).

The FAO Code of Conduct for Responsible Fisheries (CCRF) (FAO, 1995) sets out a number of obligations on States to conserve stocks and avoid over-exploitation. To achieve this, they are required to collect data so that decisions are based upon the best scientific evidence available (FAO, 1999). Rather than being prescriptive about the data and information that should be collected, broad obligations are set out (Box7). The precautionary approach to fisheries management requires managers to be cautious when the state of the resource is uncertain, for example when fishery data are insufficient or unreliable (FAO, 1999). Straddling and migratory stocks

This precautionary approach is embodied in the CCRF as well as the 1995 United Nations (UN) Fish Stocks Agreement. The latter is a binding instrument which applies the precautionary approach both on the high seas and within Exclusive Economic Zones (EEZ) for straddling and highly migratory stocks. Annex 1 of this agreement specifies the minimum data requirements that Flag States are obligated to collect (and share) for the management and conservation of these resources. The basic requirements include:

Because of the characteristics of these resources (highly migratory with poorly defined boundaries) they are not suited to local (community) management. These resources are therefore likely to be most effectively monitored and managed through coordination by the states involved. Convention for the International Trade in Endangered Species (CITES)

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is an international treaty which was drawn up in 1973 to protect wildlife against over-exploitation and to prevent international trade from threatening species with extinction. Member countries (146) act by banning commercial international trade in an agreed list of endangered species and by regulating and monitoring trade in others that might become endangered. Exports of endangered species (see Appendixes I to III of the Convention) require a valid export permit containing the information set out in Resolution Conference 10.2 (formerly Appendix IV of the convention). The production of these data is likely to be the responsibility of a country's customs and export departments. Convention on Biological Diversity

The Convention on Biological Diversity was established in 1993 in response to the world community's growing commitment to sustainable development. The objectives of the convention are “…the conservation of biological diversity, the sustainable use of its components and the fair and equitable sharing of the benefits arising out of the utilization of genetic resources, including by appropriate access to genetic resources and by appropriate transfer of relevant technologies, taking into account all rights over those resources and to technologies, and by appropriate funding”. Countries that have ratified the agreement are obliged to identify and monitor through sampling and other techniques “…components of biological diversity important for its conservation and sustainable use” and “Maintain and organize, by any mechanism, data, derived from identification and monitoring activities” (Article 7). However, no advice is given with respect to required measures or indicators of diversity. Several measures or indicators are likely to be appropriate to the fisheries sector based either upon catches (e.g. species richness, presence/absence etc) or abundance data (e.g. CPUE data) (Section

3.2.5 Data requirements in support of memberships to regional management bodies

International reporting responsibilities usually exist as a result of either membership to one or more commissions set up to harmonies and promote rational and responsible management of fisheries resources on a regional or global level, or ratification and compliance with international conventions or codes of conduct.

Membership to many of the regional bodies or programmes, agencies, organizations and commissions such as the Organisation of Eastern Caribbean States (OECS), Integrated Development of Artisanal Fisheries (IDAF) programme; Southern African Development Community (SADC) and the Mekong River Commission (MRC), often requires the provision of data and information. These data may be specific, determined by a combination of the nature and structure of the local or regional fisheries and the objectives for management and development.

More generic information requirements to meet the reporting responsibilities of the main international commissions and conventions are described below:

FAO Regional Fishery Commission Requirements

Countries that are members of FAO regional fishery commissions including the:

These commissions have been established to promote management of fish stocks in the commission or convention area. Members of the UN or any of these commissions are required to report to the FAO Fisheries Department the following information (FAO, 1999):

  1. Nominal (liveweight) catch statistics for the countries' flag vessels that fish in the area.4 These should be broken-down by species classified in accordance with the FAO Common and Scientific names (See Section 3.1.2). Routine monitoring programmes (RMPs) are the main sources of these data.
  2. Annual production of fishery commodities, imports and exports. These should be expressed in terms of country, volume, value and processing method in accordance with the FAO International Standard Statistical Classification of Fishery Commodities (ISSCFC) (see FAO, 1999 for further details). The production of these data is likely to be the responsibility of a country's customs and export department.
  3. Fleet statistics Member countries are also required to complete a questionnaire each year detailing their fleet statistics. These refer to the “...number and total tonnage of fish catching, processing, and support vessels utilized in commercial, subsistence and artisanal fisheries by size of vessel measured in gross registered tonnes (GRT) and by type of vessel according to the International Statistical Classification of Fishery Vessels (ISCFV)” (FAO, 1999). These data are generally available from frame surveys (Section and or vessel registers (Section 3.4.1) and included in management plans (Section 3.3).
  4. Employment statistics. Employment statistics are also requested each year by means of a questionnaire. These refer to the number of workers according to the time devoted (full-time, part-time, occasional) to fishing and aquaculture, by gender (FAO, 1999). Employment statistics are typically collected by means of frame surveys and population censuses undertaken by government line agencies such as Bureaus of Statistics (BS) and should also be included in management plans.

3.3 Category 2 - Data To Formulate and Coordinate Local Management Plans

The concept of management plans was introduced in Section 2.2. Management plans (MP), usually presented in a report or logical framework format (see, serve as a reference and information source for those stakeholders involved in the management of the resource. The formulation of the plan must therefore be undertaken with the full participation of these stakeholders (see Section 5.2). Local stakeholders are also likely to be the main source of much of the information required to formulate the plan. Categories of information in the plan might include those below. Berkes et al. (2001) also describe typical elements of management plans (see

3.3.1 Resource and environment

  1. The stocks or fishery being considered and the area under the jurisdiction of the LMI. This might include information on the relative importance of each species exploited measured in terms of catch weight or value determined from local knowledge or by more formal monitoring programmes. Attempts should be made to categories species according to their migratory behaviour (e.g.. sedentary or migratory). Once the plan has been implemented, much of this information will be generated by ongoing monitoring and evaluation activities.
  2. Information on environments, habitats or locations critical to the life history of the stock or species. This information is useful for designing management strategies and might include the location of spawning and nursery areas, migrations routes, and water-bodies where fish survive during the dry season. This information could be assembled on the basis of consultations with local resource users, or based upon the results more formal (spatially referenced) monitoring programmes.
  3. Potential catchment influences on the fishery or stock, identified from maps or satellite images (see Section 3.3.10).

3.3.2 Fishery

A co-managed fishery may simply comprise a number of homogenous fishers operating similar gears in one location, as is the case in some Caribbean fisheries (see Halls et al., 2000). In most cases, however, the fishery will be more complex, consisting of one gear type but operated by teams of fishers belonging to different socio-economic categories; or different types of boats or vessels operating different gear types in different locations. A management plan and its evaluation needs to consider the effects of these different categories of fishing economic units (FEUs) [see Box 8] on the resource and the impact of the management plan on them (FAO, 1997; FAO, 1999).

The Fishing Economic Unit (FEU)
The Fishing Economic Unit (FEU) typically comprises the fishing craft (if any), the fishing gear, and the fishermen to carry out fishing operations.
(Bazigos, 1983)

The management plan should, therefore, contain the following information for each category of FEUs: (i) total numbers; (ii) gear types and technology employed; (iii) some idea of the selectivity of the gears with respect to the species and size of fish caught; (iv) seasonality of fishing; (v) location of fishing; (vi) landing locations; and (vii) socio-economic categories of fishermen and other stakeholders associated with, or dependent upon the different categories of FEUs. Most of this information can be compiled with the help of local resource users represented by the LMI and intermediaries often as part of frame surveys or participatory appraisals (see Section 4.3). Once the plan has been implemented, much of this information will be generated by ongoing monitoring and evaluation activities.

3.3.3 Fishers and other stakeholders

Management actions may have a different impact (e.g. the distribution of income) on stakeholders. Attempts should therefore be made to identify distinct socio-economic categories of fishers (professional, subsistence etc), their sub-categories (e.g. women, children) and other stakeholders (fish traders, leaseholders etc) corresponding to or dependent upon different FEUs. This profiling will usually be undertaken as part of a frame survey, participatory appraisals or periodic socio-economic surveys.

3.3.4 Management roles and responsibilities

Details of all stakeholders involved in the management of the resources, including their roles and responsibilities and planned activities (see Section 2.3.1). Stakeholder analysis described in Section 5.2.1 provides a means to identifying stakeholders, their capacity and respective interest in the management of the resource as the basis agreeing these roles and responsibilities and for identifying opportunities for information sharing.

3.3.5 Management plan objectives and current status

This might include: (i) The agreed biological, social and economic objectives for the fishery. These should be consistent with the overarching policy objectives and goals; (ii) The current performance of the management plan in realizing these objectives, and the impact on the resource and its users (biological, economic and social impact); and (iii) Data and information concerning non-compliance. Management objectives and corresponding indicators to evaluate the performance of the management plan are considered in Section 3.5 below.

3.3.6 Management strategy

  1. Details of management control measures (e.g. closed seasons, mesh size regulations, effort restrictions etc) and interventions such as stocking or habitat enhancement/rehabilitation programmes employed to realize the management objectives. This should include details of user or access rights, existing legislation and sanctions for non-compliance. The rules and regulations may need to comply with national legislation and any management obligations resulting from international or regional management agreements, or ratifications of conventions, codes of conduct or voluntary instruments (see Section 3.2.4).
  2. Details of exiting monitoring (data collection), control and surveillance programmes and activities including who is responsible, what information is collected, how, when and where and associated costs. Known strengths and weaknesses of the existing systems should also be documented (Section 5.2.8).

3.3.7 Performance evaluation criteria and decision-making arrangements

Details of the indicators and criteria used to evaluate the performance of the management plan in relation to the specified management objectives, and to adjust or refine the management strategy as necessary. This might also include procedures for consultation and joint decision-making among stakeholders. Details of any models or analytical approaches (including explanatory variables) used to guide decision-making might also be included here (See Section 3.5 for further explanation).

3.3.8 External arrangements, markets and vulnerability context

Details of relevant legislation, cultural factors, markets, (seasonal) prices, trade arrangements, donor assistance, population, economic and technological trends, and the frequency and predictability of natural disasters. All these factors have the potential to affect fisher behaviour and ultimately management performance (see Section 3.5).

3.3.9 Results of any previous management plan evaluations

A summary of the results of any previous evaluations of the management plan should be included to support the re-formulation or revision the plan. This may include the outcome of among fishery or unit comparisons of management performance (see Section 3.5.4).

3.3.10 Data to coordinate local plans

Effective coordination of local management plans by appropriate administrative levels of the fishery department (and intermediaries) to minimize negative interaction among local management strategies is an important role to maximize overall management performance and minimize conflict among LMIs and their communities. For example, in river systems, the use of barrier traps in the channel may need to be coordinated or restricted to minimize conflict among communities exploiting migratory species. Activities that may impact on the environment such as potential destructive fishing practices may also have to be managed in a similar way.

The ability to monitor and coordinate these interactions requires full knowledge of the details of each local management plan. Mapping important attributes of each plan together with details of existing fishing operations and methods by means of a Geographic Information System (GIS), could provide an effective means of identifying potential interactions and identify sites where coordination is required or where enforcement activities should be focused (see de Graaf et al., 2003 and Meaden and Do Chi, 1996 for further guidance). This mapping approach might also be used to identify potential sectoral interactions to facilitate a more integrated approach to management.

3.3.11Other information

The management plan may also contain details of costs and benefits in order to justify the expenditure on the various components of the management system. Costs may include administration, and staff and capital equipment for monitoring, evaluation, control and surveillance. Benefits are often less easy to quantify, particularly where they result in social or conservation, rather than economic, returns. See Cochrane (2002) for further discussion on the design and implementation of management plans.

2 Particularly with the support of donor projects or programmes.

3 FIDI classifies “full-time” fishers as those receiving at least 90 percent of their income from, or spend at least 90 percent of their time in fishing. “Part-time” fishers receive between 90 and 30 percent of their income, and spend between 90 and 30 percent of their time in fishing. “Occasional” fishers receive less than 30percent of their income form fishing and spend less than 30 percent of their time in that occupation.

4 Data concerning the nominal catch of fish included within FAO species group 36 (tunas, bonitos and billfishes) are reviewed in collaboration with regional tuna agencies ICCAT, IATTC, IPTP, SPC, etc.

Previous Page Top of Page Next Page