Accuracy and bias of visual estimates of numbers, size structure and biomass of a coral reef fish

Abstract

Accuracy and bias of visual estimates of the number, size structure and biomass of juveniles of a small coral reef fish, Salarias fasciatus, were examined on 10 artificial patch reefs at One Tree Reef, Great Barrier Reef. Visual estimates of the number and length of flsh were made by 3 observers for 3 different durat~ons (5, 10 and 15 min) at each reef. These estimates were compared to measurements obtained from subsequent destructive sampling of reefs (98.5 % of flsh collected from all reefs). An increase in census penod does not continue to improve the accuracy of the estimates of numbers of fish The accuracy of the 10 min census did not differ significantly from the 15 min census, although it was signif~cantly higher than the 5 min census. Also, observers had unique biases for specific size-classes when estimating the size structure of the population. Biomass was estimated from the visual censuses of one observer, using a length-weight relationship, and compared to a known bion~ass from subsequent destructive collections. The mean accuracy of the biomass estimate derived from visual estimates of lengths was 72.3 %. This level of accuracy was obtained when the length-weight relationship was good (r2 = 0.97) and the estimates of lengths of each size-class (mean accuracy = 86 %, pooled for observer, times and sites) and the estimate of numbers (mean accuracy = 91.690 for the 10 min census) both had high levels of accuracy. But such high accuracies may not b e expected for censuses of larger mobile species. The use of visual censuses to estimate the biomass of such fish populations are likely to b e subject to greater inaccuracy. Most of the error in the visual estimates of biornass was explained by the inaccuracies of estimating the length of fish visually. Categorising fish into 5 mm sizeclasses or using a length-weight relationship to convert length to biomass did not significantly affect the accuracy of the estimates.

Impact of observers’ experience level on counts of fishes in underwater visual surveys

“…However, the simple log-linear models we applied fit our data well (r 2 -values are relatively high and residuals are generally well balanced around lines of best fit throughout the experience range from which we have data), have the benefit of simplicity and consistency (the same model was applied to all data sets), and share the essential characteristics of any model we might reasonably apply, namely, that small to moderate increases in experience tend to have a great impact on observers' counts at the low end of the experience spectrum, but only rather marginal, if any, impact on the performance of experienced observers. Certainly, as experience levels increase, other sources of difference among individual observers (St John et al 1990) will become much more important than relative experience.…”

“…However, it is unwise to assume that underwater visual surveys accurately estimate actual population sizes (St John et al 1990, Jennings & Polunin 1995, and, in any case, for survey programs which aim to detect differences among sites or time periods, it is only necessary that surveys give good relative measures of density or diversity. Therefore, to remove experience bias, it would be sufficient for survey programs to broadly balance observers' experience level among surveys.…”

“…In summary, observer experience level is a potentially important source of error in underwater visual surveys, particularly for harder to count taxa. While experience level is certainly not the only factor causing differences among observers (St John et al 1990, Thompson & Mapstone 1997, it is relatively easy to identify situations in which it might be a source of bias. Resource managers and scientists in charge of survey programs should seek to avoid gross differences in experience level among teams of observers involved in surveys at different sites or times.…”

“…Inter-observer variability, i.e. consistent differences among observers in the number of fish or species they tend to count, appears to be common (St John et al 1990, Kulbicki & Sarramegna 1999, and therefore differences in the make up of survey teams among sites or times could bias results. Differences in intra-observer variability, i.e.…”

Reef fish assemblages: a re-evaluation using enclosed rotenone stations

“…The inherent biases and errors associated with visual estimates have been examined numerous times (e.g. Davis & Anderson 1989, St John et al 1990, Watson et al 1995. Overall, visual censuses tend to underestimate fish densities (Sale & Douglas 1981), especially those of small (Bellwood & Alcala 1988) and/or cryptic species (Brock 1982).…”