4. INDICATORS, DATA TYPES AND VARIABLES

4.1 INDICATORS AND VARIABLES
4.2 SPECIFICATIONS FOR SELECTION OF VARIABLES AND DATA
4.3 INDICATORS AND ASSOCIATED DATA TYPES AND VARIABLES

Once policy and management objectives are defined with their relative reference points, appropriate performance indicators can be identified, and so can the variables which are needed for their estimation. However, there is feedback between choice of indicator and data variables, since it is at this stage that logistics and costs have a significant influence on the data collection programme. Besides the demands of an indicator, choice of variable is influenced by: · the operational characteristics of the fishery which dictates what can feasibly be collected; · the total number of variables which can realistically be collected; · the number of indicators which a variable can be used for; · how often the data needs to be collected; (or the variable needs to be sampled) · the expected data quality and quantity that can be obtained; · issues of standardisation. However, the primary factor is the link between the necessary operational, biological, economic and socio-cultural indicators and their associated variables.

Any designer of a data collection programme should identify the appropriate variables that are both feasible to collect, and that can provide the relevant indicators for management. The data variables discussed below are neither exhaustive nor are they all required in any particular fishery. It is up to the programme designer to decide upon the variables needed, based on the objectives and indicators that have been chosen.

4.1 INDICATORS AND VARIABLES

Performance indicators measure the effectiveness of fishery management actions implemented to meet policy objectives. They broadly lead to three categories of representation:

· simple trends in absolute values such as of catch or employment;

· qualitative and quantitative changes in infrastructure/institutional arrangements which affect management outcomes, such as changes in access right system or degree of participation by fishers;

· trends in relative values [not between the absolute value and its related reference points such as Maximum Sustainable Yield (MSY) or Maximum Economic Yield (MEY)].

Biological indicators can be used to monitor the state of exploitation of the fishery, but are inadequate to assess the performance of the fisheries sector as a whole. Economic indicators can measure the relative importance of the fishery to the nation or region at the macro- or micro-economic level. Socio-cultural indicators take into account the diversity of needs and practices of different groups of people within the fisheries sector. Compliance indicators are needed to monitor the effectiveness of management measures and reduce conflict. In practice, fisheries assessments should always combine biological, economic, socio-cultural and compliance indicators to guide management decisions.

The identification of policy priorities and management issues are largely dependent on the identification of problems in the fishery. A number of performance indicators exist which can help to identify these problems, suggest courses of action and monitor the results.

Variations in indicators alone (such as CPUE) are of limited use. These variations can be interpreted most usefully for decision making when they are related to reference points as either targets (e.g. maximum economic yield or MEY, or fishing effort at MEY) or limits (e.g. minimum biologically acceptable level of spawning stock biomass - MBAL).⁵ The indicators themselves are often easy to calculate from routinely collected data on their component variables, but the reference points are generally estimated using stock assessment methods. Together they provide information on the status of the fishery and on the performance of the management system.

⁵ A Target Reference Point (TRP) indicates a state of a resource and/or the fishery which is considered to be desirable and at which management, whether during development or stock rebuilding, should aim. A Limit Reference Point (LRP) indicates a threshold in the state of a resource and/or a fishery, which management should ensure the fishery never falls below.

A critical concern for any data collection is consistency. In many cases, it is imperative to have long time series of data collected consistently and routinely in order to evaluate trends in the behaviour of a variable. This has long been accepted practice with biological data, but has often been ignored for economic and socio-cultural data.

4.2 SPECIFICATIONS FOR SELECTION OF VARIABLES AND DATA

4.2.1 Evaluation of the operating characteristics of a fishery
4.2.2 Data type priority
4.2.3 Frequency of data collection
4.2.4 Data quantity and quality
4.2.5 Standardisation

4.2.1 Evaluation of the operating characteristics of a fishery

Prior to the data type selection and survey design, it is essential to evaluate the operating characteristics of each fishery. It will also be important to update this information when fleets or vessels change (e.g. from foreign to domestic, artisanal to semi-industrial, or freezer to wetfish trawlers). There is no single method for such evaluation since it depends on the type of the fishery. Nevertheless, a direct and full appreciation of daily fishing operations is fundamental to the data collection design. For example, an examination of fish handling practices is necessary to decide what level of species detail should be feasible for reporting in fishing logbooks. This is one of a number of points in the management process where involvement of fishers and other industry representatives can be helpful.

4.2.2 Data type priority

The collection of information from the fishing industry may be an onerous task, particularly where poor relations exist between the industry and the authorities. Compliance with data supply and willingness to assist in data collection are the two biggest administrative problems for management. The industry often sees the provision of data as time-consuming, pointless and/or a release of information that would be beneficial to others. It is clear from experience that two attributes of a fishery enhance the ability to collect accurate and timely data:

· the general trust between those fishing and the authorities (including data confidentiality);

· the ease with which data can be collected, compiled and distributed.

In developing the data collection system, the implications for fisheries management of not collecting certain types of data will also need to be considered. For example, production data with detailed species, product and size grade information may be needed for the dynamic bioeconomic models necessary to set optimal quotas. However, if such types of data are found too expensive to collect, management may need to reconsider the use of quotas as a control for the fishery.

Following some decisions on what data are possible to collect, it is then necessary to decide on what data are essential, and what are only desirable. Catch and effort data are critical to construct the most important indicators in most fisheries. Other data types relating to details of vessel activity, may not be considered necessary in particular cases.

When beginning data collection systems, initial emphasis should be on the harvesting sector for all data domains (operational, biological, economic and socio-cultural), with processing and other secondary and tertiary sectors being constructed subsequently according to available resources and management goals.

Each data type may be used for a variety of indicators. Catch, for instance, may be used both in calculations of revenue for economic purposes, and as a rough measure of resource depletion. Using various models, effort can be linked both to fishing costs and to fishing mortality. This is useful because it is not possible to measure these variables like costs and mortality directly all the time, if at all.

Because different indicators may make different uses of the same types of data, attention should be given to recording data in a manner that allows their use for different purposes. For example, data on effort, an economic variable, should ideally be recorded in a form proportional to variable costs of fishing, such as travel distance and number of fishing days. Alternatively, for compliance control purposes, the fishing position may also be needed. For biological purposes, effort data may be needed by set or haul and in a form proportional to fishing mortality.

The selection of a data type also depends on the available analyses. Many fish populations dynamics models require catch in weight and number by species, as well as other data on the biology of each species (e.g. age). A bioeconomic model may require data not only on the specific fishery production and prices, but also on other economic sectors for comparative purposes.

For socio-cultural data, the essential starting point is data on individuals fishing. Fish dealers and processors are the next most critical group. Data collection on other interested parties (such as consumers, environmental organisations, coastal developers, etc.) can be added as funding becomes available. However, the level of detail both required and available will vary. Some data may be acquired from existing routine data collections, such as fishing licences or permits and census records. Other data may need to be collected through new programmes.

4.2.3 Frequency of data collection

The frequency at which variables should be measured and data should be collected depends on their rates of change and the costs of measurement. Most variables require a natural data collection frequency, which often becomes apparent when the dynamics of the fishery are understood. The following are some broad categories of data collection frequencies:

· Very frequent: usually collected by automatic recorders (e.g. VMS), such as time, position and sea temperature. The volumes of data can become inordinately large, and some pre-processing is necessary before the data is stored. Depending on the use of the data, the frequency may be reduced to a single daily record.

· Daily: usually provided from industry records (e.g. logbooks, processing records) covering catch, effort and processing rates.

· Trip: the majority of harvest related data can be reported at the end of each trip, including landings, a trip summary of effort, fishing grounds, prices, trip costs, and other operational and micro-economic data. Although many variables are naturally collected by trip, not all trips need to be covered, but a sampling strategy can be used (see section 5.6) to reduce costs.

· Monthly: measurements based on months are appropriate for variables that change slowly and those that have a seasonal pattern. This does not include average monthly values, such as prices or catches, which are derived from more frequently collected data, but could include data obtained from an external source, such as retail price index, or monthly rainfall.

· Annual: this is used for slow moving variables, such as investment in gear and vessels. Commonly, registers and licences, which can be updated annually, are used for this purpose.

· Infrequently collected data: other types of data can be collected at periods greater than a year. These include household and demographic information as well as habitat degradation, which may be updated every 3-5 years. If necessary, inter-survey periods can be estimated using interpolation, which is adequate for most purposes.

4.2.4 Data quantity and quality

Assessing the state of resources, their potential for exploitation, and preparing options and advice for fisheries management requires reliable fishery data. The extent to which this can be conducted effectively is almost always limited by the quantity and quality of the data. Whereas simple analyses based on minimal information can provide useful indications for management, sophisticated analyses which consider options for exploitation (e.g. gear type, foreign or national) whilst taking account of technical and biological interactions between resources, are immensely data demanding. The move towards more sophisticated analytical methods, which provide better management advice, is necessary to improve fisheries management. The foundation of improvements in fisheries management is an accurate data set collected using efficient methods.

Computer simulations can be used to determine the quantity and quality of data required for each indicator in which the variable is used. The accuracy of variables and cost of collection can be estimated for each of a number of scenarios. The data collection programme can then be designed to limit the statistical error and, hence risk, to an acceptable level.

4.2.5 Standardisation

The initial set-up of system standards and classifications has to take into account not only immediate data collection needs, but also the evolution of the data collection system and data needs over time.

The primary objective of standardisation is to facilitate integration between different data collection systems. A data collection system serving one purpose may have to be integrated with others having different aims and scope. All these systems may share, to a varying degree, a number of common statistical components such as species and boat/gear classifications.

Requirements for variables and the strata in which the related data are collected are different at different levels (e.g. local community, local government, central government or international). These different requirements should be examined in order to avoid duplication. The data should always be collected at the level of the most detailed stratum, as it is always possible to aggregate, but impossible to disaggregate data. For example, if fish length-frequency data were collected aggregated over each landing day instead of trip, it may turn out later that on different trips vessels were exploiting different stocks. As the length frequency cannot be linked to particular trips, it would no longer be possible to know from which stock they originate and stock assessment work using these data would be unreliable.

When setting-up species, boat/gear or other classifications it is good practice to take into consideration other statistical systems that may be using similar categories. Logical linkages and cross-references can then be established between different classifications, making direct comparisons possible.

Structural changes to the classifications in the middle of a processing cycle should be avoided because it might create confusion, duplication of data or allocation of data to the wrong categories.

4.2.5.1 National and regional data standards

⁶ FAO Fisheries Department web site at: http://WWW.FAO.ORG/FI

4.2.5.2 Requirements for creating subregional and regional databases

There are instances when it is essential to bring together fishery data collected by means of different national programmes for the purpose of conducting research on the state of shared stocks. Such integration is feasible under the following conditions:

· all contributing national standards and classifications share a common regional or inter-regional set of statistical standards (usually at a high level of aggregation), and that each national database is equipped with the necessary logical linkages for reporting data at that commonly used level;

· all estimated data (such as totals on catch and fishing effort) are recorded in compatible computer media and utilise the same exchange formats;

· automated procedures are in place to speed up the integration process and generate (with minimum or no manual intervention) a regional or inter-regional statistical database capable of performing typical reporting functions;

· national data are compiled from the raw data so that the national statistics can be further aggregated to international requirements in terms of variables, data stratification, and standards.

4.3 INDICATORS AND ASSOCIATED DATA TYPES AND VARIABLES

4.3.1 Fishing and operational indicators
4.3.2 Biological indicators
4.3.3 Economic indicators
4.3.4 Socio-cultural indicators

When choosing the data to be collected, it is necessary to establish explicitly the link between objectives and goals, performance indicators and the data types and variables necessary to generate them. These links have implications not only for data collection, but also policy. If a policy requires increasing employment, but the responsible agency is unable to collect the necessary data to assess employment, the policy performance cannot be reliably assessed. There is no prescription for selecting data types and variables but this must be based on needs and local circumstances.

There are many possible data types beyond those discussed here. However, the examples given should cover the most important ones. It is not suggested to collect data on all the types mentioned. Choice of data should be clearly justified based on their use. Data are collected to generate indicators necessary for policy and management, therefore the expense of data collection, as part of management costs, needs to be justified.

Many of the variables can be used for more than one type of indicator (e.g. catch and effort). This contributes to determining their importance and priority in data collection. In some cases, important data types are used in a number of different assessments as they measure a commonly used factor. For instance, catch is both a measure of the benefit to society and "cost" to the resource, and hence occurs in both economic and biological indicators. In others, increasing the available data often allows existing indicators to be refined. For example, gross value of production can be converted to gross value added and then to resource rent as more detailed cost information becomes available.

4.3.1 Fishing and operational indicators

4.3.1.1 Total catch: landings and discards

Catch in numbers or weight represents the removal of biomass and individuals from the ecosystem, and is the fundamental impact fishing has on fish populations. Catch data are necessary for most stock assessment techniques. Catches should be broken down into categories with as much detail as possible. The priority classification of catches should be by species. Assessment of combined species yields have to rely on methods based on general ecosystem production, which by themselves are unreliable. If catches can be further broken down into categories based on size, maturity, location and date of the catch, it may be possible to develop a wide range of assessment methods leading to more reliable results. A detailed breakdown can also improve economic and socio-cultural analyses.

The interpretation of changes in catch is very difficult without additional information on the status of the stock. High catches may be unsustainable, and low catches can result from exploitation rates both above and below the optimum. Additional information on the stock status, such as an index of abundance or size composition of landings, is required to obtain a true assessment of the fishery. Invariably a long time series of comparable catch data is required for any reliable interpretation.

Where discarding takes place, catches will not be the same as the live weight equivalent of the landings. Discarding has significant biological implications and should always be recorded or estimated. Total catch consists of total landings and discards.

Transshipping at sea must not be neglected in monitoring catches, otherwise a considerable proportion of the overall catch may be unaccounted for. Every effort should be made to identify where transshipping is taking place and to monitor it with on-board observers. If this is not possible, contact should be made with the authorities of the receiving vessel Flag State to seek their assistance in obtaining the transshipment data. Similarly in inland fisheries, transshipping from fishing boats to transport vessels must also be considered.

Variables and sources

In most cases, it is useful to obtain catch both in weight and numbers. Conversion from numbers to weight (or vice versa) can be obtained through an estimate of the mean weight of individual fish caught. Length measurements may also be converted to total weight of the catch, if a reliable length-weight relationship has been established beforehand. Similarly landed weights for products resulting from primary processing at sea (gutting etc.) can be converted to live weight equivalent (also called nominal catch, whole weight or round weight) once a reliable relationship is established.

In general, catch data should be detailed enough in terms of time-area strata to allow them to be aggregated to stock units. It is not always possible to group landings and discards by stock as often stocks cannot be well defined, although they can sometimes be delineated by season and area. Categories in practice may be based on species (or species group), fleet, season and fishing area.

It is important to know what the target species is as this can help in understanding vessel activities. Often catches of target species (or major species) are recorded accurately, but by-catch species are either neglected or reported aggregated as groups, particularly when those by-catches are discarded. In the light of increasing concern about the effect of fisheries on ecosystems, recording by-catches (whether retained or discarded) at the lowest possible level of aggregation is important.

Table 4.1 Examples of catch and discard variables

Data Type	Variables
Target species/species group	species (or species group)
Total catch	weight; number; number of baskets/bins/boxes; holds (volume)
Species composition	sampled fish species; number of baskets/bins/boxes/holds by species
Average size	sampled fish species, length, weight; catch weight by size gradings
Discards	species; weight; number of baskets/bins/boxes; whole/macerated

Table 4.2 Examples of production variables

Data Type	Variables
Product types	whole round/green; gutted; boned; headed; fins off; fillet; skin on/off; loin; mince; surimi; fish meal (from whole fish/discards/broken or sour/offal etc.); consumer packs
Conversion factors	standard conversion factors from processed to whole weight by product type (above)
Product storage	whole frozen; IQF; hold frozen; storage temperatures; dry; brine; salted; fresh
Product packaging	individually marked and packed (e.g. tunas); carton (type and weight); bag (type and weight); basket (type and weight); barrel
Package contents	non-fish weight (ice, glaze, salt, packing material, coatings, liquids, sauces etc.); fish number; package weight; product type; size grade
Processing machines	machine type; production rate

Catch weight estimation during fishing operations may be dependent on the experience of the fisher. Catch weight by species in a cod-end, in the collection bin, across baskets or pumped into holds will always be a subjective estimate and can vary widely in accuracy between fishers. It is often possible to refine measurement for the landed part of the catch (e.g. counting of boxes). Refining such estimates can be undertaken either at landing or following on-board processing. In the latter case, it may be useful to record actual production on the daily log. In industrial fisheries, many fishers maintain these records for their own or company purposes anyway. Thus, processing methods, product types and their methods of storage and packaging will also be useful to audit catch and landing records.

Total landings can be obtained from logbooks, sales slips or interviews with fishers or intermediaries. Discard estimates can sometimes also be obtained from fishers. Data from on-board observers during fishing trips may be valuable where detailed trip information on discards and fishing locations are not usually available. Information recorded by observers should be largely the same as that at landing sites, but in more detail with additional relevant information on the vessel operations.

4.3.1.2 Effort

Effort in biological assessments is used to estimate fishing mortality. Fishing mortality is a fundamental variable in stock assessment, representing the proportion of the stock that is removed by fishing. Effort is used in setting most fishing controls. In economic and socio-cultural analyses, effort can be related to fishing activity, vessel and fleet profitability and economic efficiency. Changes in total fishing effort may be an indication of stock status or fishing profitability but, like changes in catch, are difficult to interpret without additional information from other biological, economic and socio-cultural indicators.

To record fishing effort requires careful thought on how effort will be used and how it may be collected practically. To relate effort to fishing mortality for use in biological models, it is necessary to relate it very closely to specific gear use, such as trap soak time or time trawling. On the other hand, to relate effort to profitability requires data at the trip level, including time spent at sea, time fishing, and labour and capital inputs.

Variables and sources

Annex 2 provides a more detailed list of measures of effort, in order of priority.

Usually not all effort is of the same type on a trip. Time spent fishing, searching for fish or travelling to fishing grounds for fish should be distinguishable. Search-related information should be noted, such as the number and type of tuna schools/aggregations encountered and what they were associated with. Fishing effort could also have a 'success' attribute, particularly in trawl and net fisheries, to enable complete or relative discounting of any one 'set' when analyses are done.

For active gears, like trawls, the size, number and times of operation may be required. For passive gears, like traps, the soak time for each gear should be recorded. If these data are not available, an average case will have to be assumed. For instance, if only trap boat days are recorded, it would have to be assumed that on average boats have pulled a fixed number of traps with the same soak time throughout the time series. If this assumption is untrue, the resulting analyses could be incorrect.

To relate costs to effort, effort needs to be categorised by types of capital inputs (e.g. gear types, wheelhouse electronics, processing equipment) and types of labour (e.g. fishers, processors, cooks, mechanics). Many of these data are available from vessel and operations data (Table 4.6). Here too an average case may have to be assumed for all effort (i.e. a fixed cost per unit effort) if appropriate information is not available.

Where available, sightings of fishing vessels form an important source of information on vessel activities. It can be used to verify effort data from other sources, or estimate effort directly. The use of sightings data depends on extent of the coverage and the level of detail in the accompanying information (e.g. precision of location or the extent to which activities were recorded).

Table 4.3 Examples of fishing gear variables for identifying gear types and characteristics

Data Type	Variables
Gear	gear type (bottom trawl, dredge, mid-water trawl, purse seine, gillnet, longline, pole and line, jiggers, traps, beach seine)
Construction	mesh(s); material; hook size; doors; TED; grids; burst panels; escape doors; diversions
Size	length; depth; headline; foot rope; hook spacing; total line length
Deployment	bottom; midwater; surface; fixed; anchored; free floating; association (log/school/FAD/birds/seamount/convergence)
Subsidiary vessels	dinghies; scout; net boat
Electronics	beacons; netsonde; mass sensors
Markings	gear number; vessel identification
Bait	type of bait used in association with the gear (in traps, on longline hooks, etc)

Table 4.4 Examples of fishing effort variables

Gear Type	Variables
All gears	time steaming; time fishing; number of labour by type; types of gear; electronics; other capital inputs
Trawl and dredge	date, times, speed, positions (lat/long, location id, grid id, depth) for gear "set", "on bottom", "at school", "closed", "off bottom", "haul start", "on surface"
Purse seine	date, times, positions (lat/long, location id, grid id) of start set, end set, pursed, pumped/brailed, on board.
Longline	number of hooks set; date, times, positions of start set, end set, start haul, finish haul
Trap	number of traps set; date, times, positions of start set, end set, start haul, finish haul
Vertical nets	number and length of strings set; date, times, positions of start set, end set, start haul, finish haul
Pole and line and jiggers	number and type of poles; number and type of jigging machines; date, start time, end time, position (lat/long/depth) of operation
Beach seine	length of net, date, start time, end time

Table 4.5 Examples of sightings variables

Data Type	Variables
Identifiers	vessel; permit or licence number as displayed on the hull of the vessel
Location	latitude and longitude; fishing ground; statistical area; management area
Activities	steaming; fishing; setting gear; hauling gear
Offences	fishing without licence; fishing in a closed area; fishing out of season; lack of proper vessel identifiers; gear type, mesh size and fish size infractions; misreporting catch

4.3.1.3 Catch per Unit Effort (CPUE)

CPUE or catch rate is frequently the single most useful index for long term monitoring of the fishery. It is often used as an index of stock abundance, where some relationship is assumed between the index and the stock size. It can also be used in monitoring economic efficiency.

It may be dangerous to rely on CPUE alone as a stock size index, particularly in pelagic fisheries. It is commonly assumed that the index is proportional to stock size and that the stock size changes according to a particular population model. Verification of these assumptions requires additional data.

Another problem arises with changes over time of fishing efficiencies or operational patterns, which will require that the index be adjusted. Routine surveys of gears, such as those obtained from frequent frame surveys, should help to cope with this problem.

CPUE alone cannot determine economic efficiency or vessel profitability. Additional data are needed on costs and earnings.

Variables and sources

CPUE should be separate for each stock unit and gear type. In practice, separate CPUE indices may only be possible for each species (or species group), fleet, season and fishing area. In general, as large a number of variables that affect the catch rate should be recorded alongside catch and effort. These variables can then be included in analyses, so CPUE can be adjusted to reflect only those effects that are of interest.

CPUE can be calculated directly from vessel landings, when catch is recorded by unit of effort. However, generally both catch (Table 4.1) and fishing effort (Table 4.4) are recorded separately and CPUE is derived from these data. It is important to recognise that there may be many different measures of fishing effort that can be collected, so a number of alternative measures should be available from the variables recorded. This ensures the most appropriate unit of effort can be used in each analysis.

4.3.1.4 Fishing operations

Fishing operations indicators describe the composition of fishing fleets and fishing patterns and are the basis of most management decisions. They are important for monitoring compliance and in analyses involving fishing effort. For instance, mapping fleet activities by gear use allows management to detect infringements of zone allocations or potential conflicts in gear use (e.g. trawling versus gill net) which require zoning.

Linking fishing operations to socio-cultural, infrastructure and other economic data improve analyses of fleet activities. Such analyses produce a better understanding of motivations in the behaviour of different fleets, so more accurate predictions can be made of the fleets response to changes in the fishery.

Variables and sources

Fishery operation variables refer to information on types and number of gears, fishing location, vessel speed and direction. Fishing gear requires careful monitoring because fishers will continuously improve their gear. Their objective is primarily to increase their catch rate or decrease their operation costs, and hence decrease their costs of production. Fishers secondarily aim to comply with regulatory mechanisms that may be imposed, in particular to minimise catch of illegal size classes and species.

Most fishing vessels whose activities are the target of complete enumeration will operate under a licensing regime or vessel register. Many of the necessary data for monitoring fishing vessel activities come directly from fishing vessels, for example through logsheets, observer reports, inspectors, landing enumerators or Vessel Monitoring Systems. Data on operations can be linked to vessel characteristics by unique identifiers, such as call sign or licence number. Registers generally are the primary sources of data, but problems with coverage and updates can mean that this information needs to be collected through direct measurement for crosschecking or filling in gaps in the data. Logbooks, questionnaires and interviews can also provide additional information beyond the basic operating variables, such as cost or crew demographic data.

Table 4.6 Examples of fishing vessel variables

Data Type	Variables
Identifiers	vessel name; ship registration number; international radio call sign (often used as the unique primary key); vessel fishing licence or permit number; captain's name; fisher licence number
Type	vessel type (e.g. trawler, purse seiner, longliner, pole & liner, canoe)
Power	inboard/outboard; sail; engine(s) horse power; generator
Size	GT; NRT; load capacity; length; breadth
Crew	number by grade or job description
Gear	the identification of the nature of the fishing gear used (sometimes several types within one fishing day) can be difficult, but will be essential if accurate estimation of fishing effort is to be undertaken
Operations	trip number; trip start/end date and time; operations (in port, steaming, fishing, broken down)
Support craft	helicopter; scout; dinghies; associated fishing vessel (pair trawling)
Storage	type (e.g. dry hold, brine tank, freezer); capacity; temperature
Freezing method	brine, plate, blast
Communications	type (e.g. radio, telephone, internet); contact information (number, address)
Other electronics	type (e.g. GPS systems, sonar, echosounders)

For some vessels, data on fishery operations can be recorded by a computer directly from bridge instrumentation. Electronically gathered operations data can also be transmitted automatically to databases through satellite or ground communications.

4.3.1.5 Offences and prosecutions

Changes in the number and types of offences could indicate a change in the patterns of compliance, offering insights into the effectiveness of management measures or changes in fishing patterns due to stock/market conditions. The various laws and regulations are designed to put policy and management decisions into practical management measures. Preventive enforcement activities encourage fishers to comply with these measures, benefiting the community as a whole. A lack of compliance, for whatever reason, may suggest that the policy or management decision needs to be reconsidered or adjusted.

Cross-references with socio-cultural and economic data will assist in identifying the fisheries where particular economic or cultural incentives are creating more significant compliance problems. Analyses may also suggest ways to address these problems.

Variables and sources

Data are needed to identify vessels, gears and fishers and associate them with specific types of illegal behaviour and with patterns of non-compliance. Although the number and type of recorded offences is a first indication of the level of compliance, the results of judicial activities provide a guide to the effectiveness of surveillance and enforcement. Thus, measures of the number and types of warnings, prosecutions and convictions and the nature and scale of penalties should be recorded, including warnings, summary convictions (admission of guilt), licence or fishing activity suspensions, fines, confiscations and imprisonment.

For interpreting statistics on offences, logistical data, such as the number of patrols, numbers of vessels examined and area searched, are also necessary. Declines in offences, for example, may be due more to decreasing resources for enforcement than increased compliance by fishers.

Table 4.7 Examples of offences and prosecution variables

Data Type	Variables
Identifiers	vessel name; registration number; international radio call sign (often used as the unique primary key); vessel fishing licence or permit number; captain name; crew member names; fisher licence number; flag state
Prosecutions	number by type of offence and level of judicial proceeding
Convictions	number by type
Type of action taken	warning; fine; jail term; revocation of licence; confiscation of vessels/gear/fish catch
Departure and destination	dates; ports
Reason for passage request	travelling to fishing ground; ferrying passengers
Enforcement logistics data	number of vessels searched; number of vessels fishing; number of vessels observed on patrol; date, time and area searched

Data on illegal vessels and fishing operations can be collected at sea from sightings (Table 4.5). Data on catch, such as infringements of minimum size or quota controls, can be obtained at landing sites. Data on judicial proceedings can be obtained from court records.

Normally sightings data are collected by air surveillance, although this can occasionally be done by sea patrols. Aircraft are flown at regular intervals over designated zones to spot illegal intrusions and illegal fishing, or even to spot domestic vessels to verify their reported positions.

Another source of data is the transit or innocent passage request. As a fishing vessel crosses into a coastal state's EEZ on the way to or coming back from its fishing ground, it is normal practice for the captain to report to the authority of the coastal state. Changes in the number and type of requests for innocent passage will enable surveillance and enforcement activities to be altered in response. This information may also be very useful for the country where the vessel is ultimately going to fish. Data will be needed to identify the vessel, its point of departure and planned destination, and the time spent in the waters of the state that is granting transit or innocent passage.

4.3.1.6 Dissemination of compliance information

Without knowledge of the limits to allowable behaviour, fishers may inadvertently act in ways that are damaging to the rest of the fishing community. The timing of information transfer to stakeholders (fishers, processors, regulatory agencies etc.) will vary according to the particular fisheries management requirements. Static rules defined by laws may require infrequent communication compared to the annual distribution of quotas or effort limits. Indicators of the effectiveness of information transfer will include changes in the level and type of information disseminated, measured through the number and type of communications, directly through extension/information services, or indirectly through the newspapers, magazines, radio and television.

These levels should be compared with those for offences and prosecutions. Cross-referencing with socio-cultural and economic data may identify fisheries where current methods of information dissemination are inadequate and assist in finding the most effective modes of communication for those fisheries.

Variables and sources

The types of data to be monitored will include numbers, types, and locations of information bulletins distributed, and to whom they were targeted. Any feedback from the target audience should also be recorded. The agency broadcasting the information should be the main source for monitoring dissemination. Periodic surveys with fishers and the public will measure the effectiveness of information transfer.

Table 4.8 Examples of compliance information dissemination variables

Data type	Variables
Dissemination format	circulars; radio messages; visits by fishery officials
Numbers disseminated	numbers by format, location, and target audience
Locations disseminated	vessels; processing facilities; fisheries offices; local fishers co-operatives
Audiences covered	fishers; processors; market dealers
Feedback	numbers of replies by type; current knowledge of fisher households and the general public on management issues

4.3.1.7 Stock enhancement

Capture fisheries, particularly of inland waters, are increasingly being subjected to practices designed to increase the stock size and productivity of the fishery. Such practices include releasing juvenile stages produced in hatcheries into the wild, the introduction of fertiliser to lakes and reservoirs, the culling of predator species and the construction of artificial reefs to provide habitat for certain exploited species.

Variables and sources

Data variables in the initial stage of the enhancement project include the number of individuals at each specified life stage released to the wild, and quantity and type of fertiliser applied to a lake. At later stages, data variables required would include levels of lake production and numbers of individuals re-captured alive; these data are needed to judge the biological effectiveness of the enhancement efforts. Data on costs and benefits will also be needed to judge the cost-effectiveness of these programmes.

Data sources include stocking agencies such as fishery authorities, hydroelectric power companies and sport fishing clubs.

Table 4.9 Examples of stock enhancement variables

Data Type	Variables
Fish production level	number of fish by species and age, at introduction and subsequently
Nutrient level	number of fish at and post-introduction of fertilisers
Costs	finance for research and development, implementation, monitoring

4.3.2 Biological indicators

Biological performance indicators of an exploited stock are often based on results from fish stock assessment. A good stock assessment should separate the different factors that lead to changes in catches and catch rates, such as gear or equipment used, crew size and skills, location, stock size or other changes in the fishery or environment. Stock assessments can provide estimates of stock size, fishing mortalities, yield-per-recruit⁷, spawners per recruit and other indicators. These indicators require reference points for their interpretation, also obtained from stock assessment methods.

⁷ Recruits: Fish entering the fishery for the first time, i.e. they are recruiting to the fishery at certain size or age.

Fishers often possess very detailed knowledge on species lifecycles, abundance and distribution in time and space. This indigenous knowledge is frequently reflected in local management practices. While these fishery-specific data must be integrated with data available from fisheries science, they should not be dismissed simply because they are derived from outside the scientific establishment. When such local ecological knowledge is dismissed, not only are important data lost, but it encourages confrontation, inhibiting effective management.

4.3.2.1 Stock size

Stock assessment generally aims to estimate the current stock size and its potential for increase in size. These results can be used to predict future stock sizes based on a range of possible management measures (quota, effort limitation). In the simplest case, all the fish in the stock are assumed to be the same, so sex, size, maturity and other species are ignored.

The number of fish in an ocean, sea, lake or river at any time depends upon the previous number of fish together with those factors that cause it to change. The changes can be attributed to natural and fishing mortality, recruitment, immigration and emigration. A stock is so defined as to exclude immigration and emigration (i.e. a self-contained fish population). Models of recruitment and natural mortality need to be assumed, while fishing mortality can be estimated using catch data.

A number of the indicators on stock size are used to define the state of a stock and the controls necessary to conserve it. For example, replacement yield is the estimated current population growth, such that if that quantity of fish is caught there will be no overall change in the population size; it can be used to set overall quotas. Current fishing mortality can be estimated relative to that which would obtain the maximum sustainable yield, which could be used to set a limit to fleet expansion. Similar combinations of indices and reference points can be used to set limits on effort, numbers of licences and other controls relevant to management objectives.

Variables and sources

To estimate stock size requires a time series of the total catches (including discards) and an index of stock size. The time series should ideally be complete since the start of the fishery. Even if data are incomplete, the total catches will have to be present or estimated for the entire period as these are used in the population model and provide proxy estimates for resource potential and variability. They are a measure of the impact the fishery has had on the stock.

CPUE is often used as the main indicator of stock size. The catch and effort series need not be complete over the life of the fishery, but the more catch and effort data there are, the better the assessment will be. This is because CPUE is used as an index to link observations to the underlying fish population model, rather than an integral part of the population model itself.

An alternative indicator of stock size is obtained from scientific surveys of biomass (e.g. trawls or acoustic surveys). Scientific surveys are independent of the fishery, and so avoid many of the problems of bias which occur with CPUE indices. However, they tend to be expensive and therefore little data may be available. Combining results of scientific surveys and CPUE of the commercial fishery is often the best course.

As stock size is strongly affected by annual recruitment, recruitment indices, provided by regular egg, larvae or juvenile surveys or an environmental index (such as rainfall or upwelling strength), may be necessary. Stock can be estimated either as numbers of fish or as biomass (the numbers of fish multiplied by their mean weight). If catches are only measured as weight, assessment methods based on numbers of fish will require the mean fish weight.

In many cases it will be necessary to identify individual stocks (self-contained fish populations) for stock assessments. While this can be done using special research projects, routinely collected biological data including samples of meristic characters, parasites, blood samples, number of vertebrae or spawning season will help separate one stock from another.

Catch and effort variables are available from vessel activity and landings data. Recruitment indices may be collected through fishing surveys, larval collectors or be obtained from outside sources. Other scientific data would be collected on sample surveys undertaken by the responsible scientific institute or agency.

Table 4.10 Examples of stock size variables

Data Type	Variables
CPUE	catch (Table 4.1); effort (Table 4.4 )
Scientific survey data	location; volume of water fished; volume or area searched; biomass detected
Catch	total catch in numbers and biomass by species (i.e. total removals from the ecosystem)
Recruitment indices	environmental variables; direct surveys of larvae
Stock identification	morphometric variables; measures of difference from mDNA and electrophoresis

4.3.2.2 Stock Structure

While overall stock size is of greatest importance, the stock status can be more accurately assessed if some account is made of the stock structure, such as age, sex and maturity. Even if the overall stock size is large, there should be some concern if the sub-population of mature females were more heavily depleted, as this may have a future impact on recruitment. The methodologies used are similar to those for determining stock size, except additional variables are now needed to break down the catch into categories.

Indicators can use variables related to stock structure to assess the status of the population. In general, as the rate of exploitation increases, the mean size of the fish in the population and in the landings becomes smaller. This may have two consequences. Firstly, fish may be caught before they reach their optimum market size, so potential economic gains from fish growth are lost (growth overfishing), particularly if larger fish have higher prices per kilogram. Secondly, fewer fish recruited to the stock have a chance to reach maturity and spawn. This can lead to recruitment failure in later years (recruitment overfishing).

Common indicators, such as yield-per-recruit or spawners-per-recruit, attempt to indicate the current rate of stock production in terms of growth and recruitment. This can guide managers in whether the fishing pressure is too great to be sustained. More simple analyses provide information on spawning season, spawning grounds and nursery areas.

Analyses combining both stock structure and stock size, such as tuned Virtual Population Analyses (VPA), provide particularly powerful indicators on the status of the stock. However, the data demands for these methods are high, requiring all catches to be broken down accurately into age and/or size categories.

Variables and sources

Size and/or age structure provides the critical information on stock structure. Age can be either observed directly through counting growth rings or derived from size using a growth model. Conversions from size to age frequencies are best accomplished using an age-length key, which is derived from aged sub-samples of the full size frequency. Because of inter-annual changes in growth and reproduction, it is recommended to establish length-weight relationships and age-length keys for each year, if possible.

The stock sub-populations that may need to be particularly monitored are those of recruits and pre-recruits, mature stock, breeding females. Maturity measures should always be accompanied with length measurements to be able to detect the size-at-first-maturity. However, obtaining the sex and maturity of a fish is not always easy. Some species may even change sex as they grow, and many species may change their size of first maturity downwards as fishing pressure increases.

Size composition data are relatively easy to collect by sampling vessel catches. Most often a standardised length measure is recorded. Large length frequency samples are needed for a good stock assessment. A sub-sample of individual body weights is often considered useful as it allows routinely gathered lengths to be converted to catch weight, necessary for yield-per-recruit and other growth based analyses. In some cases fish may be graded by size for commercial reasons, so landings and market records may prove a useful source of these data. Where fish are landed by size categories, it is necessary to sample all categories and to apply raising procedures that lead to accurate estimates of the total length composition in the catch. For the application of VPA and similar methods, all length or age data must be raised to the total landings.

Table 4.11 Examples of stock structure variables

Data Type	Variables
Age	otolith rings; scale rings
Size	fish weight; fish length
Sex and maturity	sex (based on internal or external characteristics); gonad state

4.3.2.3 Species community structure

While no widely accepted multispecies stock assessment techniques exist, resource assessment analyses sometimes include some provision for biological interactions (predation and competition between species) and technical interactions (differential species selection by the gear).

A change in species composition of the exploited community is an indicator of overall health of the ecosystem. Such changes may be interpreted, inter alia, through changes in abundance of ecologically important species (keystone species), overall species diversity and changing mean trophic level.

Information on incidental catches, including those of aquatic birds, reptiles and mammals, which are not retained by the fishers, provides an indication of mortality inflicted on these species not represented in the landings. These data are important for assessing the impact of fishing on the ecosystem as a whole.

Variables and sources

Catches should be recorded separately for each species, or as fine a taxonomic grouping as is practicable. This may be achieved through species composition sampling or complete enumeration where species are separated for the market.

Market grades can still be used, but present some problems in interpretation, depending on how species have been grouped. Where groups represent higher taxonomic units, such as genus or family, some interpretation can be placed on changing relative species frequencies. If grades are limited to groups such as 'trash fish', they have very little value in this regard.

Stomach contents may be sampled from fish to obtain indications of interactions between species, but this is not usually done as part of a regular sampling programme. However, observers on fishing vessels are sometimes required to collect information on stomach contents.

Landings and market records will reflect commercial groups, which usually follow taxonomic categories, although maybe not to species level. Observers, logbooks, interviews, and research surveys may be used to provide species composition data.

Table 4.12 Examples of species community structure variables

Data Type	Variables
Species taxonomic groups	species names; species in commercial groupings
Species composition	catch in numbers and weight by species
Species interactions	stomach contents

4.3.2.4 Environment

Environmental information to be used in relation to other information on the stock (such as catch and effort) will be important in a number of studies, particularly where there is a direct link with environmental effects and landings, such as with the main upwelling fisheries or inland floodplain fisheries. Important limnological, oceanographic and meteorological data may be used in a range of analyses, including ground-truthing of remotely sensed data.

Although fishing is often a major factor in determining fish abundance, populations will fluctuate whether exploited or not. Natural fluctuations of ecosystems are not fully understood by researchers and therefore fluctuations in stocks cannot be predicted as accurately as desired. To separate the different effects, a long time series of data is needed covering periods of significant change in the variables for both stock size and environmental effects. Depending on the analysis used, at least 15 years data may be required for reliable results.

Variables and sources

A large number of variables could be listed that would give information on the various habitats or ecosystems. General variables include: water level, area flooded and topographical information in riverine and floodplain fisheries; salinity gradients in mangroves and coastal areas; seasonality and gradients in temperature.

Logbooks may contain information on some environmental variables. Many environmental variables are routinely collected by various governmental institutions: topographical maps, satellite images, automatic-recording buoys at sea, etc. Much information on the environment is available through scientific research.

A Vessel Monitoring System (VMS) can be useful for collecting certain environmental data that relate directly to fishing operations. With modern, reliable interfaces between sensors and computers, it will be feasible to collect a variety of environmental data with minimum cost and error. Such data can be collected and stored at smaller time intervals between measurements than crews have time for, and can provide a major source, perhaps partially replacing expensive research platforms.

Many types of environmental information, such as meteorological data, should be collected by other institutions or agencies.

Table 4.13 Examples of environmental variables

Data Type	Variables
Oceanographic/limnological	water temperature profile (at surface/on bottom/at gear); currents (speed and direction); sea state (wave height); sea colour; nitrate concentration; oxygen concentration; PH; salinity
Meteorological	rainfall; air temperature; wind (speed and direction); ice formation

4.3.3 Economic indicators

A number of measures exist that have been used by various agencies to measure the economic significance of fisheries to the national and local economies and assess performance of fisheries management in achieving economic objectives. The key macro-economic indicators include the gross value of production, the gross value added, the level of subsidies, the level of employment, the balance of trade and foreign exchange earnings. The first four indicators can also be applied at the regional or fishery level. Key microeconomic indicators include the level of resource rent, the economic performance of fishers and changes in the level of investment. These indicators are evaluated at the level of the fishery or the individual fleet segments within a fishery.

Policy-makers also need to be aware of changes in the level of consumer demand in the economy. Changes in demand will affect the prices received by fishers (although the final consumer does not generally purchase from the fisher), having an impact on their performance and the value of the fishery to the broader community.

The economic performance of the fish processing sector may also be important in some countries. The continued existence of some fisheries may depend on a viable processing industry. Measures similar to those used for assessing the harvesting sector may also be applied to the processing sector.

4.3.3.1 Market prices

Market prices at the various market levels are short and medium term indicators of the demand for fish products. They signal changes in markets and, if properly interpreted, provide hints to the future commercial operations of the sector. Prices are also necessary for the calculation of many other economic indicators.

Analyses of factors affecting prices are important when formulating fisheries policies. For many fish species, their price is a function of a number of factors, including landings, and the landings of other species that may be close substitutes in the market. Management policies that change the mix of landings [such as Total Allowable Catch (TAC) for particular species] will change prices, and therefore the total revenue and profitability of the sector.

The responsiveness of price to changes in quantity landed is a useful measure when looking at the implications of management controls that affect landings. The price response to supply can be estimated from prices received and quantity landed for the domestic and/or export markets. However, this relationship may be dependent on additional factors, such as prices of competing food items and the level of imports. All market variables may also be affected by other macroeconomic variables, such as inflation or the exchange rate, so use of price data in this way may require a good understanding of the economy as a whole.

Prices based on market structures can be useful in policy formulation. Significant differences in prices between regional markets could indicate barriers to entry (e.g. lack of transport facilities). Similarly, large differences between prices paid to fishers and prices paid by consumers could indicate market imperfections (e.g. collusion by buyers). Once identified, these problems may be addressed through changes in policy.

Variables and sources

Local, national and regional market prices should be collected by the appropriate government agencies. Information at the level of international markets may be collected through the various FAO services concentrated in Globefish and on the Internet.

Table 4.14 Examples of market price variables

Data Type	Variables
Price of products	price by species (or species group), market grade, market level (harvest, processor, wholesale, retail; local, national, regional, international)

4.3.3.2 Gross value of production (GVP) and of processed products

The gross value of production (GVP) is determined by multiplying total production by the price received. GVP provides an indication of the potential economic importance of a fishery relative to other fisheries or other industries in a nation or province. However, increasing GVP could represent either a worsening or improving long-term state of the fishery. To account for this, the change in the value of the remaining biomass of the stock could be deducted (or added in the case of a stock increase) from the calculated GVP.

Gross value of production can be broken down into the gross value of processed products. This provides information on the level of economic activity of the fisheries processing subsector with respect to the other fisheries subsectors and the rest of the food processing sector. It is the result of multiplying the value of each respective type of product by the volume produced in a given time.

Variables and sources

For the harvest sector, information on volume and value of production can be obtained from landing sites (e.g. landing sales slips, logbooks). The volume of final production of the industry can be obtained from sales and production records. Other data may be obtained from diverse sources depending on each particular situation, including post-harvest facilities, national statistical authorities and customs records.

Table 4.15 Examples of GVP variables

Data Type	Variables
Production	landed weight by product type; processed weight by product type
Prices and unit values	value of output by product type

4.3.3.3 Costs and earnings

Profitability is a vital micro-economic indicator of fishery performance. Improving the incomes of fishers is often an important fisheries objective. Information on boat profitability provides a measure of performance in achieving this objective, as well as providing an indication of economic sustainability. The same indicator can be derived for the processing sector. However, with harvesting and processing becoming more integrated, it may not always be possible to fully separate these sectors. To remain viable in the short-term, fishers, processors and others must be able to cover all of their cash costs. Hence, a measure of financial profitability of different vessels and facilities provides an indication of short-term sustainability. To stay in the fishery in the longer term, operators need to meet all costs and therefore economic profitability is the more appropriate measure. This includes the non-cash costs such as the value of their own labour, and the depreciation of capital. In addition, they must achieve a return on the investment, which is at least as much as that which could be earned elsewhere in the economy. Otherwise, new investment will tend to be diverted to other sectors, which are expected to yield a higher return. In the short term, however, the existing capital is effectively sunk, so vessels and facilities will continue to operate as long as positive rates of return are being achieved, even if return on the investment is low.

When examining economic profitability, the treatment of cash costs also differs. Pecuniary payments (e.g. interest, rental and lease payments) are not included as these represent transfers rather than real resource costs. These are compensated for by the introduction of an allowance representing the expected return on investments. Loan repayments (while an important financial cost) are also not included in the measure of economic profitability. These are compensated for by the inclusion of depreciation charges, which account for the capital consumed in the fishing activity.

Table 4.16 Examples of profitability variables

Data Type	Variables
Revenues	sales-quantity and price by market grade or processed grade
Fixed costs (vessel)	insurance (hull, property, workers compensation, health, protection and indemnity); professional fees (accounting, legal, bookkeeping, tax filing); loan payments (principal and interest); finance/service charge; vessel depreciation; all other gear depreciation (fishing gear, electronics); storage; leases; repairs and maintenance of hull, engine, equipment and fishing gear; haul out; overhaul; dockage; vessel permit fees; fishing licences and fees; office expenses; association fees; cold storage rental; on-shore costs (processing, holding); lease, fees or rent of onshore facilities
Fixed costs (processor)	insurance (property and casualty, business interruption, workers compensation, health, protection and indemnity etc.); professional fees (accounting, legal, bookkeeping, tax filing etc.); loan payments (principal and interest, finance/service charge); depreciation; administrative salaries; taxes (income, property etc.); plant improvement costs; advertising; permits; bad debt allowance; storage; leases; repairs; maintenance; office; taxes (income, property); office expenses; association fees; cold storage rental
Variable costs (vessel)	fuel; oil; bait; ice; water; total food cost; trip, grading/handling/unloading; on-board processing costs; packaging material; local transport costs; supplies; labour costs (crew, number, crew share formula, total crew cost, total captain cost, non-monetary compensation estimated value, non-monetary compensation distribution formula, captain and crew bonuses); onshore employee salaries
Variable costs (processor)	labour (number of full-and part-time employees and cost); utilities; transportation; raw product cost; packaging material; additives used in the production process; waste amelioration, water (quantity and cost); local transport costs; supplies
Assets and financial flows (vessel)	current assets (list and value); long term assets (lists and estimated market value); current liabilities (list and amount); long term liabilities (list and amount); annual income all fishing sources; annual cash-flow all destinations; sources of financing; total other annual revenue from use of vessel; amount and value of quota or fishing effort bought or sold; market value of processing plant; land; equipment
Assets and financial flows (processor)	long-term liabilities (list and amount); income from all sources; cash outflow from all destinations; value of stocks, market value of plant, land and equipment
Technical information (vessel)	type of vessel; length; gross and net tonnage; hull construction material; hold capacity; engine (age, power, fuel type); harvest gear; deck gear; gear-mounted electronics; on board processing/refrigeration (capacities/description); year built; purchase year and price; estimated market value fully equipped; market value of permits owned; number of vessels within the group; market value of onshore investment (e.g. storage areas, vehicles, workshops)
Technical information (processor)	plant identification and activities; primary markets; plant capacity; degree of vertical integration; degree of horizontal integration; equipment inventory; types of waste amelioration; total number of workers; total numbers of support staff; domestic fish purchased; domestic fish imported; production hours; inventories; quantity and value of output by product form and by customer

Both stock assessment and socio-cultural analysis may be needed if profitability is to be properly interpreted. As with the measure of GVP and resource rent, the use of vessel profitability as an indicator of economic performance needs to account for the biological status of the stock. Similarly, monetary profits may be distorted by socio-cultural factors. For example, in fisheries where crew and captain are kin, some cash costs may be deferred longer than would usually be expected.

Variables and sources

The main information sources are the harvesting (individual fishers) and processing sectors. However, support industries, such as fuel and fishing gear suppliers, may provide useful cost data. Many of these variables are also necessary for calculating other indicators that use costs of production (see 4.3.3.6 gross value added (GVA) and 4.3.3.8 resource rent and economic profits).

4.3.3.4 Investment

The amount of investment is one of the best indicators of changes in fishing and processing capacity. Investment can involve upgrades of existing operational capacity or acquisition of new capacity, in the harvesting, processing or marketing sectors. Each type of investment has different implications for fisheries management. Given the state of exploitation of world resources and the need for sustainability, investment in fishing fleets is of special concern to government.

Variables and sources

Official registration of investments in the Ministry of Finance (or similar authorities) should be the main source of data. Secondary sources include secondary support sectors, such as fishing gear suppliers and manufacturers, and the vessel registration system.

Table 4.17 Examples of investment variables

Data Type	Variables
Financial investment	investment by sector, type of economic unit, origin and destination
Existing incentives	financial return/profitability by fishery and fleet segment

4.3.3.5 Management costs

Management costs are government and industry expenses related to the administration and monitoring of the fishery. Different types of policies and management plans imply different needs for staff, material, and other funding for research, implementation, monitoring, enforcement, etc. As more costly policies and regulations are implemented, the benefits to be gained through use of those policies or regulations are dissipated. Thus it is important to track management costs being incurred. As well as being evaluated using their own trends, management costs are necessary for other indicators, such as economic rent.

Variables and sources

The primary data source is the fisheries administration, with other costs being inferred from industry data, such as employment.

Table 4.18 Examples of management cost variables

Data Type	Variables
Costs to government	surveillance costs; enforcement costs; training costs; administration costs; scientific research cost
Costs to industry	administration costs

4.3.3.6 Gross value added

Gross value added (GVA) is the total amount paid as returns or rent to labour and capital (and theoretically to the resource base as well, although this rarely occurs without property rights). GVA provides a measure of the increase in income after the costs of intermediate inputs into the production have been deducted except capital depreciation. It builds on gross value of production (GVP) by including all costs except labour and capital. It represents the contribution or value added to the economy by the fisheries sector.

GVA provides a measure of the economic importance of the sector in the national economy in relative terms. Depending on the coverage and the methodology used, it indicates the wealth generated by the sector in comparison with other sectors, as well as the wealth distribution among factors of production.

In many countries, the GVA is estimated by central specialised government agencies as a part of the Agricultural Gross Product (AGP), which is incorporated in Gross Domestic Product (GDP). Where GVA is measured separately, it is generally only identified for the harvesting sector. The value added by the processing and marketing sector, while also incorporated in the national GDP, is usually not readily identifiable as a separate measure. Many countries are attempting to improve estimation of GVA, as it is one of the best indicators of performance. It is very much in the interest of the fisheries sector as a whole to participate in its preparation.

Variables and sources

At present in many countries, the GVA and many of its component rents have to be extracted from raw data used to calculate agricultural GDP or estimated on an exclusive basis from cost and earnings data. Other data, such as licensing and fee information, will be available from the fisheries administration. Data on subsidies may be available in the economic government ministries and/or in the fisheries administration.

Table 4.19 Examples of value added variables (also see Table 4.16)

Data Type	Variables
Harvesting/processing revenue	value of output, prices, quantities of product, landings weight
Costs of harvesting	fuel, ice, salt, bait, repairs, maintenance, insurance
Costs of processing	cost of raw product, fuel, electricity, power and water, packaging, shipment

4.3.3.7 Subsidies

Subsidies have been used in many countries to assist in the development of the fisheries sector. However, they have also resulted in negative effects such as over-capitalisation and overexploitation of fishery resources. Identification and evaluation of the various types of subsidies in use in a nation's fisheries should support the policy formulation process.

The costs of fisheries management, if not borne by the fishing industry, may also be considered a subsidy to the sector. To evaluate this, both management costs and government revenue raised from the fishing sector need to be estimated.

Table 4.20 Examples of subsidy variables

Data Type	Variables
Subsidies	fuel rebates; financial reimbursements; vessel buyback programmes; import tariffs; export subsidies; low credit rates
Government revenue	income tax on fishers; import duty on fishing gear; tax on fish products; licence fees
Government costs	see Table 4.18

Variables and sources

These data should be available from the government economic ministries and/or the fisheries administration.

4.3.3.8 Resource rent and economic profits

Of particular interest to economists is resource rent, which is measured at the level of the individual fishery. Resource rent is the returns to the capital inputs provided by the resource itself. If the resource is not owned, these returns tend to be dissipated as a result of overexploitation. Fisheries management generates resource rent by restricting the level of fishing activity. The generation of resource rent is the main economic objective of management and represents the revenue that could be extracted from the fishery in return for the use of a community resource. However, the amount of rent extracted is a policy issue for individual governments to decide.

Therefore, rent-related indicators provide sound information for fisheries planning, policy formulation and management. The level of rent generated in a fishery relative to the maximum long-run level of rent that could be achieved is an appropriate indicator of the economic performance of fisheries management.

The potential level of resource rent in a fishery can be estimated using bio-economic models (based on stock assessments, cost and earnings data). Although initial set-up of cost and earnings studies may be expensive, subsequent updates are much less costly.

Economic profits, often used as an alternative to rent in measuring economic performance, are the difference between revenues and all costs (including opportunity costs) involved in the fishery operation. However, economic profits include both resource rent and producer surplus (effectively the returns to the skill and management of the individual fishers). Separating out these two components is generally difficult. However, it is generally accepted that changes in economic profits are indicative of changes in resource rent in a fishery. Estimates of gross economic profits can be derived from deducting subsidies, management, labour and capital costs (including opportunity costs) from the Gross Value Added.

An alternative, inexpensive indication of the level of resource rent in a fishery is the licence or quota value. These can only exist where there are a limited number of licences or quotas, which can be freely sold. While the relationship between licence or quota value and the level of rent in a fishery is not certain, these values can be expected to vary with expectations of future levels of profitability. Consequently a change in the fishery that is expected to result in increased or decreased future profits will cause these values also to change, reflecting this expectation. Licence and quota values may be affected by factors other than resource rent, such as subsidies and taxes and imperfections in the licence or quota market. Where this applies, their usefulness as indicators of economic performance can be limited.

Variables and sources

Economic rent combines the same variables as a number of other indicators, namely vessel/processor profitability, subsidies, management costs, prices and GVP. It therefore uses the same variables and sources. Where it has been decided to use licence or quota values, the fisheries administration should register transactions, and therefore be able to provide prices.

To assess the opportunity cost of labour, it may be necessary to collect information on wages and employment opportunities outside the fishing industry and the level of unemployment in the region. In countries and regions where unemployment is relatively low (e.g. unemployment rate of 5% or lower), crew payments may be an adequate reflection of the opportunity cost of labour. Average crew incomes are often higher than wages of workers in other industries having comparable levels of education and skill because of the usual risks and hardships of the fishing occupation.

Where relatively high rates of unemployment exist, the opportunities for productive employment of fishermen in other occupations might be very limited, especially in developing countries. Consequently, in these situations the opportunity cost of labour is likely to be very low but always greater than zero. A zero opportunity wage would imply that time is valueless; this assumption is usually not appropriate because many unemployed persons are in fact engaged in some productive activity such child care, home improvements and others; even where there is no productive activity, the assumption of a zero opportunity wage might be inappropriate because leisure itself is a valued activity.

The cost of capital includes economic depreciation and the opportunity cost of capital. Depreciation is a non-cash cost representing the wear and tear associated with using the capital asset, and is based on the decline in value of the asset over time. The opportunity cost of capital is the return that the investment could have earned if it had been invested in the next best industry of equivalent risk elsewhere in the economy. A low risk measure may be the return on government bonds while a range of return rates for different levels of risk may be derived from the stock market. For the fish harvesting sector, an appropriate comparison may be the rates of return from equivalent investment in agriculture.

Table 4.21 Examples of economic rent variables (also see Table 4.16)

Data Type	Variables
Production	landed weight by product type; processed weight by product type
Prices and unit values	value of output by product type
Harvesting costs	fuel; ice; salt; bait; repair; maintenance; insurance
Processing costs	unprocessed product; power; water; packaging; shipment
Opportunity costs	interest rates; rates of return on capital in other sectors; wage rates in alternative employment; unemployment rates
Subsidy and management costs	subsidies; administration; MCS (see Tables 4.18 and 4.20)
Licence or quota values	licence prices; quota prices; number of licences by type; number and size of quotas by type; number and price of processing licences by type; income from auctions of fishing rights; income from special fishing agreements (supply contracts and leasing to foreign countries)

4.3.3.9 Domestic food supply and fish consumption

Fish supply to the country and trends in average per capita consumption give an assessment of consumer dependence on fish as a food source at different national, regional and demographic levels. This is very useful in the formulation of policies on fish trade and ensuring food security.

Table 4.22 Examples of per capita food supply variables

Data Type	Variables
Landings	quantity by use (food, non-food)
Fishery imports & exports	quantity by use (food, non-food)
Conversion factors	ratio of weight of fish product to weight of protein by product and species
National population	numbers of people; fish consumption; average food consumption by food type

Variables and sources

Data originate from the harvesting, processing and marketing sectors. At the national level, fisheries administrations and ministries of the economy should participate in the collection and compilation of these data. At the international level data are received, compiled and published by FAO.

4.3.3.10 Employment level in the fisheries sector

It is useful in setting policy to know the relative importance of the fishery sector as a source of employment. The number of people employed in fishing, processing and marketing can provide information on the importance of these sectors to the regional and national economy. The indicator needs to take into account a range of factors affecting employment in the fishery sector. An overexploited fishery may well have a higher level of employment than a well-managed fishery. Similarly, falls in employment may be due to falling catches as a result of overexploitation in previous years, management policies designed to reduce effort to improve the longer-term status of the fishery, or improvements in the regional economy attracting labour away from fishing into other enterprises.

Variables and sources

Information on employment may be obtained through census, surveys and in some cases by sampling from the harvesting, processing and marketing sectors. These data are often collected by central government offices and fisheries administrations.

Table 4.23 Examples of employment variables

Data Type	Variables
Number of persons employed in fishery	employees by primary, secondary and tertiary sectors, and by age, sex and job category (fishers, crew, plant workers, intermediaries, transport, services etc), time spent in occupation
Employment in non-fisheries industries	employees in primary, secondary and tertiary sectors, and by job category, age and sex
Unemployment	unemployment nationally by region, and within the fishing community

4.3.3.11 Balance of trade of fish and fishery products

Balance of trade reflects the difference between the value of imports and the value of exports of fish and fish products. It shows foreign currency earnings and losses as a result of international fish trade. In addition, the participation, structure and current trends of the national fisheries sector in relation to the international trade position can be analysed. The identification of relevant information in the preliminary analysis of this indicator could lead to a detailed study of the fish trade and eventually to the formulation of trade policies.

Variables and sources

Information on fish imports and exports value and volume may be obtained from the responsible national financial authority monitoring international trade. Information on foreign trade of fisheries products is also compiled by FAO and OECD based on statistics provided by individual countries, and by the UN Statistical Office.

Table 4.24 Examples of balance of trade variables

Data Type	Variables
Volume of trade	quantity exported by product type; quantity imported by product type
Value of trade	value exported by product type; value imported by product type

4.3.3.12 Net foreign currency position of the fisheries sector

The net change in foreign currency reserves as the result of fishing sector activities can be compared with other sectors to determine the importance of fishing in maintaining foreign currency reserves and exchange rates. The indicator includes the gain (if any) of foreign currency from exports of fisheries products less the loss in foreign currency from the imports used in fisheries production.

Additional related indicators are the proportion of GVP that is exported and the proportion of total costs in the harvesting and processing sectors that result from the use of imports. These are used to provide an indication of the sensitivity of overall profitability to exchange rates.

Variables and sources

Import and export data are usually obtained from the responsible national financial authority monitoring international trade. Cost data can be obtained from the harvesting and processing sectors.

Table 4.25 Examples of foreign currency position variables

Data Type	Variables
Export values	value by fish product
Costs	costs of inputs imported by sector; total sector costs

4.3.4 Socio-cultural indicators

Socio-cultural indicators are critical for evaluating policies and management activities, as they measure the value of fisheries beyond their simple economic worth. However, routine data collection for socio-cultural indicators has often been neglected, reliance being placed on ad hoc political procedures which aim to represent socio-cultural views. In practice, these will not replace objective assessments of performance, and socio-cultural data should be collected routinely alongside biological and economic information.

Many socio-cultural issues may be assessed using performance indicators. These indicators tend to concentrate on issues of equity and social value, where fishing contributes to society in ways that otherwise are difficult to assess. However, in contrast with biological and economic indicators, targets and limits are not necessarily well defined (e.g. through mathematical formulations) or widely accepted, so appropriate targets and limits will depend on local fishery policies and traditions.

4.3.4.1 Distribution of fishing income

The distribution of income is a measure of equity within fishing communities, and between fishing communities and the wider society. Using economic data on fishing income broken down by socio-cultural categories can tell managers if one sub-group is advantaged over another and whether particular management measures have greater impact on any one part of the community. In conjunction with measures of overall income, income distribution can also address dependency on fishing compared to other activities and indicate how well fishers may be doing relative to average national incomes.

Table 4.26 Examples of income distribution variables

Data Type	Variables
Earnings	earnings for each crew member (e.g. catch value added, share system or wage rates); earnings for each fishing household (through fishing, fishing-related and other jobs);
Demographic data	number of members in each household; age; sex; ethnicity; target fishery or fisheries; community of residence

Variables and sources

Data are usually obtained from interviews with workers and industry records in the harvest and market sectors, and interviews in the fishing communities. Government agencies should also have relevant demographic data from national surveys.

4.3.4.2 Distribution of fish consumption

Distribution of fish consumption is a measure of food security and of social stability within fishing communities. In combination with national average per capita measures, this indicator enables policy makers to assess food security with respect to fish supply, not only of the nation as a whole, but also of vulnerable sub-groups such as mothers, children, the elderly and the poor. In combination with catch and species composition data, it can indicate which species and sizes are of critical importance to those vulnerable groups.

Variables and sources

To measure per capita consumption as distributed across important sub-groups, critical variables are landings and consumption by species, as distributed by demographic variables and geographic region. Other more general household data, such as household budgets and food consumption, may be required for the elaboration of appropriate reference points. In addition, in many cultures it is important to share and distribute the fruits of one's fishing or farming labours amongst kin or neighbours. This distribution of food or income from harvested products is a vital underpinning of the social structure as well as a traditional way of assuring food for those unable to acquire it themselves due to age or infirmity. There may also be ritual or religious requirements for eating particular fish species on certain occasions.

Household and community data from the harvest sector and (for non-fishing households) from government agencies.

Table 4.27 Examples of distribution of fish consumption variables

Data Type	Variables
Landings	quantity by use (food, non-food).
Fishery imports and exports	quantity by use (food, non-food)
Nutrient conversion factors	weight of fish product to grams of protein, by product type and species
National population	numbers of people by region, community, fleet, and demographic variables (age, ethnicity etc.)
Food sharing patterns	cultural rules for food distribution in general; specific foods required for ritual use

4.3.4.3 Nature of access to the fishery

To assess fisheries governance, the nature of access to the fishery and degree of local involvement in management must be addressed by fishery management plans. An indicator of the nature of access can be used to measure the degree of co-management and the level of trust between fishers and managers. Combined with indicators of effort, stock status and capitalisation, such information may be used to assess changes in catch and effort, and to estimate the likelihood of compliance with alternative controls.

A new fishery management plan will need to consider the current management system. To do this, data are needed to document and assess current systems, identifying strengths and weaknesses and proposing practical solutions to problems.

Variables and sources

Critical variables fall into four types:

· details of the institutional arrangements, both formal and informal, which govern access to and use of the resource;

· rules for membership in particular institutions based on demographic characteristics or community of residence;

· conflicts between competing systems (e.g. a formal and an informal system in place at the same location) or caused by the nature of the access (e.g. gear conflicts due to a proliferation of vessels under open access);

· degree of incorporation of local knowledge.

Data are needed on the institutions and procedures (both formal and informal) for fisheries management, links between local and national management and types and extent of local involvement. For example, some measure is needed of the strength of local institutions (e.g. co-operatives, tribal councils or fishers' associations) involvement in resource management, or in dealing with the market and negotiating with other stakeholders. Similarly assessments may be required of the role of customary local management regimes in determining management plans, what local self-monitoring organisations exist and the degree to which fishers' biological and ecological knowledge have been incorporated into scientific assessments.

The full procedure for making management decisions should be documented. Decisions may be influenced, for example, by current legislation and Ministers with different policy objectives, as well as by technical advice. In general, whatever organisations and people are involved, and the stages at which they have input into the decision-making process, need to be recorded. It is also important to assess logistical factors which may affect decision-making, such as the location of fisheries offices and the distance an average fisher has to travel to visit the office or attend meetings.

Data sources are mainly from harvest and community sectors, as well as the government fisheries agency itself.

Table 4.28 Examples of nature of access variables

Data Type	Variables
Institutions controlling access	type; jurisdiction; location; nature of access granted
Rules for membership	rules for each institution
Conflicts and co-operation	relations between institutions; relations within institutions
Incorporation of local knowledge	procedures for incorporation local beliefs; types of data incorporated

4.3.4.4 Demographics and fishing patterns in the harvesting sector

Fisher demographics and fishing pattern indicators can be used in assessing equity, dependence on fishing and fisher responses to changes in the fishery. Data, such as household size, income, experience and sources of financing, suggest the extent of dependency on the resource. Demographic data help to place fishers in relation to the rest of the population, and to indicate whether fishers could acquire non-fishing employment should that become necessary or desirable. With information on stock status and the nature of access, these indicators can assist managers in predicting future entry or exit and increases or decreases in effort in particular fisheries. Together with data on institutions and their membership rules, patterns of ownership or access can be tracked, which may be useful where management is particularly concerned with the viability of small-scale owner-operators and the development of property rights. Fishers' preferences for, or experience of, different kinds of fishing will influence their response to policies and regulations.

Variables and sources

Variables have to be measured separately for each fishery. Data are usually obtained from the harvest sector and fishing communities.

Table 4.29 Examples of fisher demographic and fishing pattern variables

Data Type	Variables
Fishing practices	fisheries engaged in by season, gear type, target species, fishing area (also see Table 4.6)
Fishers demographic data	age; ethnicity; community of residence; years of fishing experience; crew status
Vessel characteristics	length; gross tonnage; horsepower; on-board electronics (also see Table 4.6)
Crew composition	numbers of crew; job descriptions; basis for crew selection; other job skills besides fishing
Decision-making	crew selection; market choice; fishing behaviour; payment systems

4.3.4.5 Demographics and employment patterns in the processing and marketing sectors

Processor, marketing and support industry demographics and employment patterns can be used as an additional measure of the community dependence on fisheries. In conjunction with balance of trade indicators, the impacts of changes in domestic harvest on the processing sector at the community level can be assessed.

Market characteristics also provide an indicator of potential market reactions to changes in the fishery. Critical variables concern the behaviour of intermediaries in the distribution chain between harvesting and consumption (excluding processing), as well as the economic contribution of the market sector. Of particular concern is the freedom with which markets operate. This depends upon how decisions are made regarding transactions. For example, transactions may be based on kin relationships or agreements providing credit to fishers, which may affect prices (see section 4.3.3.1).

Variables and sources

Data sources include the harvest, processing and market sectors, as well as government agencies.

Table 4.30 Examples of processor, marketing and support industry demographic and employment patterns variables

Data Type	Variables
Employment patterns	number of employees hired by season and job category
Employee demographic data	age; ethnicity; community of residence; migrant or local resident
Facility characteristics	market or plant location; products processed by volume and value
Decision-making	employee hiring; choice of vessels to buy from; choice of other marketers or processors to sell to

4.3.4.6 Community dependence

Community dependence on fisheries is an indicator of economic and socio-cultural connections and constraints in the fishery. It may include food security considerations. This indicator can be used with other operational and economic indicators to explain economic migration into and out of the fishery. Socio-cultural dependence on fishing (i.e. the way fishing is incorporated in songs, festivals etc.) gives some measure of its non-financial value to the community.

As community dependence considers links between the components of a fishery, it is often complex and may require consideration of a wide number of variables. For example, impacts on fishing, its dependent industries or infrastructure, may constrain development of the whole sector. So, if roads are poor or local distributors lack transport to take their catch to market, then other incentives for increasing catch will not translate into more food for other regions or more money to local communities.

Variables and sources

Table 4.31 Examples of community dependence variables

Data Type	Variables
Employment	number of community members engaged in fishing and related industries
Fishery components	numbers of fishers, households dependent on fishing for food and/or income, boats, processing plants, wholesalers, retailers, and fishery dependent industries (e.g. marinas, bait/tackle shops, chandlers, fuel suppliers); infrastructure components (e.g. transport, communications); government and non-government institutions influencing the fishery
Income and fish consumption	percentage dependence on fish for food; percentage dependence on fishing and fishing-related industries for income by household and fleet
Historic and cultural capital	length of association of the community with fishing activities; festivals; statues; community organisations associated with fishing; other forms of fishing symbolism
Cosmology	cultural requirements for particular fish products; taboos for closed areas, periods or species; other specific beliefs and/or taboos related to fishing in general or specific types of fishing

It is particularly important to develop a good understanding of cultural and religious beliefs that may affect fishing behaviour. Certain days, seasons or moon phases may impose periods of rest, creating automatic closures. Holidays may involve the preparation of specific fish or other marine resources as a central item of a feast, thus creating strong market demand for those species at those times. Taboos may inhibit the local development of a particular fishery, despite high global market demand. Regulations that contradict or attempt to circumvent these local beliefs and practices are likely to meet with significant resistance. On the other hand, regulations that seek to build on and extend these practices are much more likely to be successful. It is critical, therefore, to research these beliefs and find those which have the potential to move the fishery in a desired direction.

Government institutions specifically devoted to fishing should be documented, along with any other organisations, which have an influence on the fishery. For example, there may be government credit associations that give loans for farming and fishing equipment. The town council or council of elders or other such body may have authority to open and close fallow agricultural areas and marine reserves. A local agency may require boat licences. Church or school groups may become the nexus for organising lobbying activities related to fishery regulations, or they may be vital networks for supporting wives of fishers who make long trips. Access to certain fisheries or gears may be governed by tribal affiliation or community membership.

Data are usually obtained from fishing communities, fishers, fisheries agencies and relevant government agencies.

4.3.4.7 Social status of fishing

The social status of fishers and perceptions of fishing as an occupation influences the likelihood of entry into and exit from the fishery. It is usually coupled with vessel, fleet, and post-harvest facility profitability.

Variables and sources

Critical variables can be grouped as those tied to the level of financial remuneration available from the fishery and those tied to cultural values. For the former, critical variables are fishing sector incomes and incomes from other sectors. For the latter, the important variables are more numerous. For example, the level of prestige associated with fishing as an occupation, influences whether fisheries attract new employment. Fishers may believe their way of life retains core values and have strong views on how the fisheries management system affects their ability to continue in their way of life. The level of fisher household involvement in community institutions and organisations as opposed to fisher-dominated institutions and organisations (the degree to which fishers are embedded within the larger culture) gives an indication of how isolated the fishing community is.

The views of the society as a whole regarding fishing are an important element to examine. Fishing may be considered the employment of last resort or traditionally involve activities or materials that are considered taboo or impure by many in the larger population. It may have suffered from global campaigns against overfishing, some of which depict fishers as pillagers of the ocean, or may be seen as a noble and courageous activity, pitting humans against nature in a fight to wrest food from the sea. The general image of fishers, coupled with the average wage from fishing versus other common jobs, will have a strong impact on efforts to either increase or decrease fishing activities.

Data are usually obtained from fishers, fishing communities, government agencies, fisheries agencies.

Table 4.32 Examples of fisher social status variables

Data Type	Variables
Financial remuneration	fishing sector incomes; other sector incomes; likelihood of fishers acquiring other sector jobs given their education and skills
Cultural values	relative prestige of fishing versus other occupations; degree to which fishing retains a desired lifestyle (e.g., independence, risk); whether fishers would encourage their children into the fishing industry; whether young people seriously consider employment in fishing; institutions and organisations to which fishers belong