Estimating Trout Abundance with Cataraft‐Mounted Dual‐Frequency Identification Sonar: a Comparison with Drift Diving

Abstract

We investigated the potential of dual-frequency identification sonar (DIDSON) deployed from a drifting cataraft for estimating abundance in rivers of Brown Trout Salmo trutta larger than 20 cm. We compared triplicate trout density estimates made by DIDSON with drift-diving density estimates in three reaches of a clear-water river in New Zealand. DIDSON density estimates were much lower (»22% of drift-dive estimates, range D 7-33%) and less precise than drift-dive estimates (DIDSON CV D 0.13-0.47; drift diving CV D 0.15-0.17). Variation in detecting fish in the DIDSON field survey contributed substantially more (95%) to DIDSON count variability than did fish detection in the image files. Highest precision with DIDSON was achieved in the reach with the least shallow habitat and most uniform channel. Fewer person-hours were required to undertake the field component of DIDSON surveys than the drift dives (5 versus 8.3 h), but the substantial time spent on image review (3.3 h) made DIDSON surveys 34% more costly than drift dives in terms of overall effort. Despite observed shortcomings, cataraft-mounted DIDSON has utility as a noninvasive survey method for estimating abundance of large (>20-cm) fish, particularly in situations where turbidity is too high for visual counting methods to be effective.

Comparison of Underwater Video with Electrofishing and Dive Counts for Stream Fish Abundance Estimation

“…In contrast to electrofishing, environmental conditions did not explain differences between dive counts and video methods. Water clarity and physical obstructions are well‐known limitations for dive counts in assessing fish abundance (Northcote and Wilkie 1963; Young and Hayes 2001; Hayes et al 2015); therefore, such environmental features may limit video methods in a similar fashion. Additionally, although we did not quantify water turbidity in sample sites, we would expect its effects to be equivalent between dive count and video methods because sampling was conducted without intervening rainfall events.…”

Monitoring fish using imaging sonar: Capacity, challenges and future perspective

“…High counting accuracy is often achieved due to the high‐quality data obtained and the single swimming direction of migrating fishes. In addition, MFLS is also widely used in surveys of habitats, such as artificial reefs (Guo et al, 2018; Plumlee et al, 2020), estuary (Becker et al, 2016; Becker et al, 2017; Lankowicz et al, 2020), river (Hayes et al, 2015; Kerschbaumer et al, 2020; Mora et al, 2015), lake (Jing, Han, Wang, et al, 2018a), estuarine shoreline (Smith et al, 2021), offshore habitat (Artero et al, 2021; Tassetti et al, 2020; Van Hal et al, 2017), reservoir (Huang & Gong, 2020; Mo et al, 2015; Shen et al, 2018), etc., to study the fish stock and interaction of fishes with the habitats. The fish density is typically calculated to estimate abundance and studied for the habitat population distribution (Plumlee et al, 2020; Zhou et al, 2014).…”

“…The fishes near the solid structures, such as seabed and monopiles, are difficult to recognize because the backscattered echoes from fish are easy to obscure by stronger echoes from these structures. Similarly, the bubbles reflect more sound waves, limiting the effectivity of MFLS in water with entrained air (Braga et al, 2022;Hayes et al, 2015;Van Hal et al, 2017). As the reflectance of fish depends on the density and surface area, the fish may be lost when swimming directly towards or away from the sonar, then appear again when the orientation changes a bit, resulting in overcounting (Kang, 2011).…”

“…Thus, the sites with this kind of substrate are not suitable for deployment (Martignac et al, 2015). Besides, imaging sonar is sensitive to the entertained air in the water, limiting its application in turbulent, aerated water and the vicinity of pump stations (Hayes et al, 2015). However, these locations are often the key sites for fish migration monitoring. Observer bias is hard to eliminate regarding counting and sizing fish even in manual operation.…”

“…The deployment also impacts the accuracy. For example, in fixed deployment for migrating observation, the narrow beamwidth cannot cover the whole passage transect (Plumlee et al, 2020); while in the mobile survey, the fish avoidance of the sonar will also cause bias (Dunlop et al, 2018; Hayes et al, 2015). A large amount of data are usually produced with MFLS, especially in long‐term monitoring, but the processing is laborious and time‐consuming.…”