Proceedings of the Third World Fisheries Congress: Feeding the World with Fish in the Next Millenium—The Balance between Production and Environment

Quality and Quantity of Fisheries Information in Stock Assessment

Yong Chen, Harshana Rajakaruna


Quantitative fisheries assessment models, based on biological theories and empirical observations, are defined by parameters that characterize the population dynamics of the stocks (Megrey 1989; Quinn and Deriso 1999). Reliable estimation of these parameters is a central issue in fisheries stock assessment and management (Chen and Paloheimo 1998; Walters 1998). Typically, parameters are estimated by fitting the models to data collected from the studied fish stock (Hilborn and Walters 1992). The quality of parameter estimation, which determines the quality of stock assessment, can be affected by many factors (NRC 1997, 1999; Chen and Fournier 1999; Quinn and Deriso 1999). Two of the most important factors are quality and quantity of fisheries data.

Quality of fisheries data is related to the precision and accuracy of measurements made on fisheries variables and is influenced by many factors, including data type; nature of fisheries; management regime; and economic, social, or ecological values of fisheries. Measurement errors are often used to determine the quality of fisheries data. Errors originating from different sources have different statistical properties. Errors in directly measuring a fisheries variable or a well-designed sampling program tend to be random in nature and small in magnitude. However, in some cases, errors associated with fisheries data can be nonrandom and biased, a characteristic of low-quality data. One example of this is catch statistics in a quota-managed system. Fishers may try to maximize their profits for a given quota by highgrading, a practice of discarding less valuable or desirable catch (usually small fish) and keeping only the valuable or desirable catch (usually large fish). In this case, only landed catch is included in catch statistics. Thus, the total catch is underestimated and the estimation of size composition is biased. Total catch statistics can also be underestimated by other harvest and management strategies—bycatch, legal size limitation, or trip limits—or by underreporting. In some cases, however, fishers may overreport catches to qualify for government assistance programs (e.g., unemployment insurance, retraining programs, etc). Overall, however, in an output-control fisheries management regime, the total catch is more likely to be underestimated than overestimated. Similar patterns have been observed for other fisheries variables (Hilborn and Walters 1992; NRC 1997).

The quantity of fisheries data describes the amount of information available for stock assessment, which can be grouped into two categories: diversity of data and amount of data. “Diversity of data” refers to either the variety of data that measures different characteristics of fisheries (e.g., total catch and age composition) or the number of sources from which the same type of data is collected (e.g., fisheries-independent and -dependent abundance indices). “Amount of data” refers to the amount of data of the same type from the same source (e.g., number of years in which age compositions are estimated for commercial catch). They may have different impacts on parameter estimation and stock assessment. Insufficient data in either category may lead to large uncertainty or even bias in estimating vital stock parameters and ultimately to mismanagement and overexploitation of stocks (NRC 1997, 1999).