Fisheries information cycle

5.1.5 Fisheries information cycle

Given that national and subnational fisheries poli- cies are derived from a consideration of all the bio- logical, social and economic circumstances that are particular to a country, it follows that the sets of data required to inform a national policy will also be unique. To enable policy to be turned into fisheries management advice that works (i.e. satis- fies the goals of policy, responds to environmental and stock variations and changes in economic development), there always needs to be an in- formation cycle that continuously supplies the knowledge system (see Fig. 5.2). At the outset, policy and its objectives need to be interpreted in terms of performance indicators such as biological reference points, against which the results of fish- eries management are measured. For example, fishery managers might ask, what is the current level of fishing mortality, a performance indicator, against the fishing mortality that satisfies sustain- ability which is a current precautionary reference point, either at the limit to biological productivity or at a target during stock rebuilding?

Sometimes fishery performance indicators may

be constructed from single variables, such as total catch, but in most cases they will be a combination of variables for which arrays of information need to be collected, for example fishing mortality. In some cases multivariable indicators may be sim- ple combinations of variables; in others, such as for fish populations, the variables may be related in complex mathematical models (see Chapters 6–13, this volume). Whatever the case, planning for the collection of the data necessary for these variables will involve determining their feasibil- ity, priority, frequency, quality and quantity, and whether standards for these can be adopted or de- rived. Once a performance indicator and its vari- ables are defined according to these determinants, the implementation phase begins through the de-

Gathering Data for Monitoring and Management

General policy decisions • Identify policy and management objectives • Incorporate the precautionary approach

WHY

Objectives of data collection • Link information needs to policy and management

• Identify types of indicators which will be required • Determine any regional requirements

Performance indicators and observable data variables • For each variable, determine whether it can be

Analytical

collected, its priority, its collection frequency, the

methods

WHAT

required data quantity, quality and standardization needed.

• Link the required indicators to variables for fishing/operational, biological, economic and sociocultural assessments.

Feedback Data collection strategy

• Obtain baseline information for design

Logistics

• Decide on complete enumeration or sampling

and

• Review operational constraints

resources

Data collection methods • Decide how the variable will be recorded • Link the required variables to their possible

sources and collection methods

Data management

HOW

• Design the database • Plan database operations and maintenance • Plan data access and dissemination

Planning and implementation • Planning phase: legal, institutional and budget

issues • Implementation phase: training, monitoring and

administration issues

System appraisal Fig. 5.2 The fisheries information

• Update and improve the programme using cycle. (Source: from FAO 1999.)

experience and feedback

velopment of a strategy, the definition of data col- Finally, a system of monitoring is required to deter- lection methods and the means to maintain it, and mine whether all this effort actually both satisfies the establishment of the legal, institutional and proper calculation of the performance indicator budgetary environments in which it will operate. and ensures that the performance indicator is ap-

88 Chapter 5

propriate to the evaluation of the effectiveness Where possible the use of standardized and inter- of the management measures. In some cases this nationally recognized units of measurement, may be relatively simple but for multivariable particularly for dimensions and mass, and indicators simulation modelling can assist this nomenclature should be adopted. The global net- feedback process by revealing both the sources of work that forms the Coordinating Working Party uncertainty and inadequacy in the whole proce- on Fishery Statistics (FAO is the Secretariat) devel- dure, and the risk or probability that these sources ops much of this work. The following interna- of uncertainty and inadequacy are important or tional classification systems form the basis for significant.

many useful standards:

At each level in the fisheries information cycle • Species: International Standard Statistical Clas- there will be many decisions, actions and the appli- sification for Aquatic Animals and Plants or the cation of resources to continuously improve the FAO 3-alpha species codes developed for commer- knowledge base. A few examples are given in Fig. cial fish.

5.2. The advantages of this are clear. Even within • Fishing vessels: International Standard Statisti- the framework of the precautionary approach, calClassificationfor Fishery Vessels (FAO 1985). ‘States shall be more cautious when information • Fishing gear: International Standard Statistical is uncertain, unreliable or inadequate. The ab- Classification for Fishing Gear (FAO 1982). sence of scientific information shall not be used as • Products: Harmonized Commodity Description

a reason for postponing or failing to take conserva- and Coding System (World Customs Organization, tion and management measures ’ (United Nations 1994). 1995). Thus, investment in appropriate informa- • Oceanography: Committee for International tion will always reduce the degree of precaution re- Oceanographic Data Exchange. quired, and hence will enable a closer approach to Many other global organizations have developed the target or limit. Assuming that analytical meth- standards that may be important to the variables ods are accurate and interpretation correct then concerned, including the World Meteorological more information means more fish may be caught Organization (climate and weather measure- or fewer controls placed on fishing activity.

ment), World Health Organization (nutritional and

Put briefly, investment in data collection and health values), International Monetary Fund analysis will increase or sustain welfare and earn (economic and financial measures), and the Inter- revenues from fisheries, because it reduces the risk national Labour Organisation (human resource of overexploitation and leads to the establishment classifications). and management of sustainable exploitation pat- terns (FAO 1999). The precautionary approach can

5.1.7 Information precision

be seen as a major incentive for the collection of re- liable and appropriate fisheries and environmental

and accuracy

information. Fisheries data and their analysis contribute signifi- cantly to the costs of national and regional fish-

5.1.6 Information standards

eries management, often equivalent to more

and classifications than 20% of gross value of the catch, in some cases

higher (Larkin 1997). The precision of the data is Part of the planning process for information collec- thus important because, in general, the greater the tion, once the management objectives and indica- precision required, the more it will cost to ensure tors have been decided, requires definition of the its accuracy. For each data type and each situation data standards and coding to adopt for different it is necessary to define the precision required both classes of variables. The use of standards assists in for the satisfaction of the variable’s requirements

a wide range of issues from data form design to and for instructions for data collection. Clearly database comparability and non-redundancy. measures of fish mass will be different for different

fisheries; perhaps to the nearest tonne for indus- trial small pelagic fisheries, or to the nearest kilo- gram for recreational angling. Similarly, the precision of dimensions needs to be established; to the nearest millimetre, say, for shrimp carapace length or the nearest metre for water depth measurement.