Salmonid Field Protocols Handbook: Techniques for Assessing Status and Trends in Salmon and Trout

Aerial Counts

Edgar L. Jones III, Steve Heinl, and Keith Pahlke

doi: https://doi.org/10.47886/9781888569926.ch19

Aerial counts of salmon are essential tools in Pacific salmon Oncorhynchus spp. management. In Alaska the first recorded aerial count of salmon was made by C. M. Hatton of the U.S. Bureau of Fisheries in the Lake Clark district of Bristol Bay in 1930. As fisheries management progressed, so did the need to cover more streams in shorter periods of time, inspiring the first systematic use of aerial surveys in Alaska by Agent Fred O. Lucas of the Bureau of Fisheries in 1937 (Eicher 1953).

The aerial survey technique is best suited for broad, shallow, clear-water systems with limited overhanging vegetation, undercut banks, and canopy cover. Aerial counts are severely compromised in glacial or turbid waters and in excessively deep water such that fish are beyond the range of visibility (Cousens et al. 1982). Species such as steelhead O. mykiss and coho O. kisutch salmon can be difficult to survey as these fish are often cryptic in coloration and have the behavior of seeking cover, even during spawning, making them less visible.

The visibility of spawning salmon to observers depends on many factors such as water quality, fish concealment, stream dimensions, and density of fish, among others (Bevan 1961). The ability of the observer to count fish accurately has been the main topic of many aerial survey studies (Bevan 1961; Neilson and Geen 1981; Cousens et al. 1982; Labelle 1994; Symons and Waldichuk 1984; Dangel and Jones 1988; Jones et. al 1998). Furthermore, biased counts of salmon abundance and associated measurement error have been seen to produce seriously biased estimates of optimum harvest rate and escapement in stock-recruitment analysis (Walters 1981; Walters and Ludwig 1981). An interesting phenomenon is that the accuracy and precision of observer counts decreases as abundance increases, and simple linear corrections for bias are not as appropriate as using allometric forms with multiplicative error structure in light of changing magnitudes of fish. In short, humans are overly conservative and tend to underestimate versus overestimate when counting objects (Jones et al. 1998; Clark 1992; Dangel and Jones 1988; Daum et al. 1992; Evensen 1992; Rogers 1984; Shardlow et al. 1987; Skaugstad 1992).

Efforts should be made to minimize the influence of extraneous variables such as weather, water quality, aircraft type, and pilot performance, and observers should minimize the impacts of these variables to the best of their abilities. The density of fish may also be an important variable. Eicher (1953), in work performed in Bristol Bay, said that the accuracy of observer counts might be inversely proportional to the density of salmon. Often, salmon can be seen packed into very tight schools, and in one study on coho salmon, fish were much easier to count once they were disturbed and disbursed, in principle lowering the school density (Irvine et al. 1992). In essence, increasing the density of salmon has much the same effect as increasing the number of undercut banks, water glare and turbidity, and canopy cover (Jones et. al 1998). Prior knowledge of the stream is beneficial with regard to accuracy when performing aerial counts. One study showed that observers familiar with the stream consistently produced more accurate estimates when compared to observers not familiar with the stream (ADFG 1964).