fishcount.org.uk

Reliability ranking for methods of estimating mean weights

Type of data (i.e. market/harvest/grow out/table size) used Reliability of
length-weight conversion data used
(if any)
Reliability Ranking
Average weight data or general/normal weight ranges Not applicable 1
Common or typical weights Not applicable 2
Average, common or typical lengths, or general/normal length ranges Good 3
Less good 4

Ranking data by reliability
Farmed fish market, harvest, 'grow out' and table size data were used to estimate the mean weight of farmed fish killed for food in 2010. Where possible, estimated mean weights used in this study were based on cited average weights or normal/general weight ranges. However, such data was often not available, and a mean weight estimate was sometimes made from other types of fish size data that are likely to be less reliable than these. On the one hand, we want to include as many references as possible. On the other hand, we donít want to use less reliable data where good data is available. In order to balance these 2 objectives, a method of ranking data according to its reliability was used.

The basic principle is that all types of data used to estimate mean weights are assigned a 'reliability ranking' ranging from 1 to 4, with '1' being the highest and most reliable. Where a species has references pertaining to different values of reliability ranking, only those for the highest ranking are used.

Ocassionally, length data was available for a species where weight data was not. In such cases, lengths were converted to weights using length-weight relationship data available on fishbase.org. For our purposes, length-weight relationship data is classed as being either 'good' or 'less good', according to how well it matches the lengths we are trying to convert. This is explained in more detail on the 'Help' screen accompanying for Screen 5 (Length-weight calculations) accessed from screen 4 (reference data details) where applicable. Whether data used to convert lengths to weights is 'good' or 'less good' affects the reliability of the data, and therefore its reliability ranking, as as can be seen in the table above.