Policy, performance indicators the main data types for the four domains, including

5.2.1 Policy, performance indicators the main data types for the four domains, including

and data variables the classes of indicators generally recognized for

the domain. (For detailed description of fishery Fishery performance indicators are needed to as- performance indicators and data variables, see sess the effectiveness of fisheries policies. If the FAO 1999.) policy indicates, for example, the requirement to increase employment, this can be interpreted in a

5.2.2 Fishery and operations

number of ways through indicators that describe the incremental growth in numbers employed or

information domain

the relative growth of the employed in relation to This domain includes all the features of the pri- general population growth, whether community, mary capture and processing of aquatic resources: regional or national. Employment numbers them- what amounts of fish were caught, when and how selves will often be an aggregation of a number of and what operational management information is variables, including at-sea, onshore, support and required? ancillary industry workers. In other cases, policy

1 Catch and discards, production and stock may require a reduction in the number of foreign enhancement

fishing vessels or foreign crew against domestic These are the key indicators in relation to fish crew, which has been the case in many countries, stock removals and are thus essential for any un- post-UNCLOS, both developed and developing. derstanding of the nature of a fishery. In some In practice, employment policy in fisheries will cases, the only data variables available may be often be a combination of these requirements and from processed product, which then need to be may need the aggregation of data variables from all converted back to whole fish for the estimation of the relevant minor strata. Therefore, policy needs stock removals. To do this, information must be to be clear and detailed in order for fishery man- gathered on conversion factors. This class of indi- agers to determine what indicators are required, cators includes the additions to stocks from en- how to estimate them and what information to hancement programmes such as restocking or collect.

species introductions.

92 Chapter 5

physical and chemical nature of the environment. The physical means of fishery production is also All may require an enormous range of data clearly an essential element of basic fishery infor- variables, and many will require both gathering mation and covers the nature of fishing vessels and data from the fishery and experimental or survey fishing gears. Depending on the fishery, a wide information. range of data variables may be required, from ves- sel length to thickness of net twine (Prado 1991).

2 Fishing vessels and fishing gear

1 Stock size

One of the key indicators long used as a proxy for

stock size is catch per unit of effort (CPUE), which These indicators relate the deployment and activi- is derived from data gathered from the fishery. This

3 Fishing effort and sightings

ties of the means of production to the catch itself. is one of many cases where indicators are derived Fishing effort is the information recorded or de- for several reasons, and hence should be consid- rived on the deployment of vessels and gear. The ered as the highest priority. Aspects of stock dy- data variables will be different for different opera- namics that affect understanding of stock size are tions. A good understanding of the operational also included, such as knowledge of stock identifi- characteristics of a fishery is always required be- cation and recruitment. For some species, par- fore considering the choice of variable, for example ticularly the schooling pelagics such as sardines accurate position at the beginning and end of a (Clupeidae) and herring (Clupea spp.), fishery- trawl tow will be important for calculating swept independent experimental surveys may be the area but these may not be necessary for purse seine only accurate way of determining stock size or pole and line fisheries. Sightings are generally information. available from patrol vessels, aircraft or other com- pliant fishing vessels and will also contribute to an

2 Stock structure

understanding of fishing effort through informa- The dynamics and nature of stocks in relation to tion on place, length of time and type of fishing op- exploitation is dependent on understanding stock erations, including whether or not this activity structure across the life history of the species for occurred in contravention of effort regulations.

all cohorts from larvae to reproducing adults.

4 Offences and prosecutions, and the dissemina- Gathering data on the age, size, sex, maturity and

behaviour of individual fish has been the major Compliance indicators are also important for the focus for this class of indicators. Such data

tion of compliance information

management of fisheries and can be used as key in- offer the means to understand the composition dicators of the response of fishers and others to the and performance of individual cohorts in relation fishery management control measures being ap- to the whole stock, from which management plied. Information on the knowledge of control rules can be derived that prescribe, for example, rules by fishery participants may also be used to the age, size or sex of recruits, or seasonal/spatial determine the nature of offences, which can be pre- limitations such as closed spawning seasons or meditated or unwitting, and feedback to develop grounds. improved compliance measures or to change

3 Community structure

prosecution practices. Within the ecosystem, indicators of the position, importance and interactions of all species, from

5.2.3 Biology and environment

plankton to top predators, will assist in defining the general community structure. This will be par-

information domain

ticularly important when fishery policy aims to There are three key classes of indicator related to maintain biodiversity, when fishing operates on the biology of stocks: stock size, stock structure multispecies complexes or when conservation re- and community structure. Together these place quirements demand it such as in relation to marine the stock within the ecosystem, alongside the mammals and birds.

Gathering Data for Monitoring and Management

using a variety of variables, enable the develop- Very large numbers of data variables will con- ment of key sectoral growth indicators, such as per tribute to the two key groups of physical environ- capita fisheries product. mental indicators: oceanography/limnology and

4 Environment

meteorology. As with all other indicators, their 2 Investment, subsidy and management priority will be dependent on fishery and geo- All forms of financial inputs are essential for the

analysis of sector development, particularly in as- graphic requirements. In some areas, for example, dissolved gases and temperatures may remain sessing market and competition economics. They

will include direct and indirect investment in the fairly static, for example over coral reefs, while in other areas these may fluctuate widely, as in up- fishery sector which should include training, and

the costs of fisheries management, including re- welling areas. Weather, in particular sea-surface winds and solar incidence, may have important ef- search and data gathering, analysis and decision

making, and surveillance and enforcement. fects on species’ life histories. Changes in currents

or ambient temperatures may affect reproduction

3 Profitability

and survival of larvae, or the rate of primary pro- Related to the above, key performance indicators duction upon which fisheries depend (Myers, associated with the generation of capital/profit are Chapter 6, Volume 1). The gross changes, for exam- required together with the technical information ple, caused by the El Niño Southern Oscillation, on plant and machinery and their depreciation that when the enormous equatorial currents of the constitute the sector’s assets. Pacific are reversed, cause major perturbations in

the distribution, survival and growth of many 4 Distribution of food, rent and trade marine stocks, from tuna to dolphins.

Food balances originating from landings, plus im- ports, less exports, are important social and eco- nomic indicators since they will enable estimation of the changes to per capita food supply and hence

5.2.4 Economic and financial

to the contribution of the fishery sector to nutri-

tion and health. In similar fashion, economic rent Four groups of information, most of which are is developed from an appropriate combination of

information domain

common to any production sector, may be used production, prices and costs. At the national scale for economic and financial indicators, including the performance of the sector in support of the (1) the consequences of production such as price, economy needs to be measured through estima- value and employment; (2) financial support tion of the volume and value of trade and foreign which would include investment, subsidy and exchange balances. management; (3) profitability; and (4) the distri- bution of food, rent and trade. This domain is probably one of the most difficult areas for data

5.2.5 Sociocultural

gathering in integrated fishery analysis because it

information domain

requires divulgence of private enterprise financial This most neglected area of information is proba- data that are usually commercial and confidential.

bly also one of the most difficult to investigate as it

deals with the multiplicity of measurable factors The price of fish is one of the key variables, and non-measurable perceptions that make up together with catch and effort, that is used in nu- human society. It includes three general groups merous fishery performance indicators, from the of indicators: the characteristics of relationships development of bioeconomic models and refer- which include access to and dependence on fish- ence points (Maximum Economic Yield), to con- eries and the social status of participants; the de- tributions to GDP or access fees (Hannesson, mographics of fisher, processor, marketing and Chapter 12, this volume). Employment figures, support industries, and their activity patterns; and

1 Price, value and employment

94 Chapter 5

distribution of income and food. Some sociocul- fundamental to the strategies for the collection of tural indicators are amenable to the definition of data and the application of control measures. performance indicators but, unfortunately, many

are not, so that targets and limits cannot be easily 3 Distribution of income and food Key economic and social indicators of fisheries

defined. Particular national or local fisheries situa- tions, policies and traditions will affect the way in- development and management concern the ways

in which the income and food resulting from dicators are defined. resource use are distributed. Data on earnings

1 Nature of access, community dependence and by individual, household or company – and the de-

the social status of fishers

mographics of these – provide basic socioeconomic The ways in which the authorities, producer or- information from which such indicators can be ganizations and communities control access and calculated. In similar fashion, the contribution of conduct their business by the incorporation of in- fishery resources and products to food security and formation and rules into business practices, and by nutrition are also key indicators of progress. An ex- conflict resolution, constitute important knowl- ample is the data required to assess per capita fish edge under which fisheries management operates. consumption and hence diet balance. The nature and extent of dependence on a fishery is also a fundamental factor that drives or con- tributes to fishery management policy, including

5.2.6 Summary of

employment, income and consumption, and his-

information domains

torical and cultural association. For example, As with all forms of sector analysis, there is always these factors are major influences on the European overlap between the data requirements of different Union’s Common Fisheries Policy and regional information domains. For example, the numbers support mechanisms. The financial or cultural of fishers contribute to a variety of indicators, in- values that fishers place on fisheries play an addi- cluding fishing effort and catch per effort, equity tional role in the determination and evaluation of and distribution, and employment and profitabil- fisheries policy, including the likelihood of com- ity. The numbers of fishing vessels contribute in pliance with control measures.

similar ways.

Each issue, from determining fishery policy to The characteristics of fishers and others con- the means of obtaining the data, needs a full con-

2 Participant demographics and activities

tribute to the derivation of fisheries policy and sideration of how the steps in the fisheries infor- management measures, and may be important in mation cycle can be analysed and used in decision defining the structure and stratification of data making. This consideration can be undertaken by collection systems. Fishing and processing prac- addressing each issue against a hierarchy of terms. tices are often seasonal, using a variety of methods. Table 5.1 provides an example of just one eco- Fishing locations and methods for target species, nomic dimension against a hierarchy of issues that involving different sections of the fisher commu- may apply to any dimension in fisheries. nity and their decision-making processes, require an understanding of these patterns. For example, the seasonal migration of nomadic peoples to the

5.3 FISHERIES DATA

coast in Oman for subsistence sardine (Stolepho-

COLLECTION AND

rus sp.) harvesting, and involving all community

MANAGEMENT

members, overlaps with the near-shore rock lob- ster (Palinurus sp.) harvesting season which is con- Obtaining information from the fisheries produc- ducted by men. These fisheries, while conducted tion sector can be a costly and onerous task, and by the same community, involve completely dif- has often led to adversarial relationships between ferent demographics, and knowledge of these is the sector and fishery authorities. The problem

relates partly to a general lack of compatibility between individual/company/community values and those of the fisheries authority, which sets the political, legal and administrative frameworks. It also relates partly to the necessity to impose re- strictions on fishery rights, hitherto perceived as inherited territorial or stock-user rights. Addition- ally, there is the question of the aims of data collection. Without feedback on why data is required, users may question its applicability: ‘We never see the results of all this effort to collect data.’ This lack of feedback can lead to distrust, to commercially confidential data being leaked to competitors, or data being used for purposes other than the purpose for which it was originally intended, for example for legal or financial in- vestigations. Whatever the causes of conflict or discontinuity in the information relationship, it seems clear that transparency and responsibility in the overall process will tend to remove the obstacles.

Comanagement can help synchronize public- and private-sector values through commitments of both groups of the fishery sector to the tasks of fisheries management: (1) assessment, (2) setting of objectives, (3) selecting management measures,

(4) allocation among users and over time, and (5) compliance control and enforcement. The more involved fishery users are, and the closer agree- ment between them and fishery authorities, then the more successful will be the conduct of fisheries management. The development or improvement of fisheries data collection and management schemes thus begins at the strategy level.