Acoustic survey techniques are widely used to quantify abundance and distribution of a variety of pelagic fish such as herring (Clupea harengus). The information provided is becoming increasingly important for stock assessment and ecosystem studies, however, the data collected are used as relative indices rather than absolute measures, due to the uncertainty of target strength (TS) estimates. A fish’s TS is a measure of its capacity to reflect sound and, therefore, the TS value will directly influence the estimate of abundance from an acoustic survey. The TS is a stochastic variable, dependent on a range of factors such as fish size, orientation, shape, physiology, and acoustic frequency. However, estimates of mean TS, used to convert echo energy data from acoustic surveys into numbers of fish, are conveniently derived from a single metric - the fish length (L). The TS used for herring is based on TS-L relationships derived from a variety of experiments on dead and caged fish, conducted 25-30 years ago. Recently, theoretical models for fish backscatter have been proposed to provide an alternative basis for exploring fish TS. Another problem encountered during acoustic surveys is the identification of insonified organisms. Trawl samples are commonly collected for identification purposes, however, there are several selectivity issues associated with this method that may translate directly into biased acoustic abundance estimates. The use of different acoustic frequencies has been recognised as a useful tool to distinguish between different species, based on their sound reflection properties at low and high frequencies. In this study I developed theoretical models to describe the backscatter of herring at multiple frequencies. Data collected at four frequencies (18, 38, 120 and 200 kHz) during standard acoustic surveys for herring in the North Sea were examined and compared to model results. Multifrequency backscattering characteristics of herring were described and compared to those of Norway pout, a species also present in the survey area. Species discrimination was attempted based on differences in backscatter at the different frequencies. I examined swimbladder morphology data of Baltic and Atlantic herring and sprat from the Baltic Sea. Based on these data, I modelled the acoustic backscatter of both herring stocks and attempted to explain differences previously observed in empirical data. I investigated the change in swimbladder shape of herring, when exposed to increased water pressures at deeper depths, by producing true shapes of swimbladders from MRI scans of herring under pressure. The swimbladder morphology representations in 3-D were used to model the acoustic backscatter at a range of frequencies and water pressures. I developed a probabilistic TS model of herring in a Bayesian framework to account for uncertainty associated with TS. Most likely distributions of model parameters were determined by fitting the model to in situ data. The resulting probabilistic TS was used to produce distributions of absolute abundance and biomass estimates, which were compared to official results from ICES North Sea herring stock assessment. Modelled backscatter levels of herring from the Baltic Sea were on average 2.3 dB higher than those from herring living in northeast Atlantic waters. This was attributed to differences in swimbladder sizes between the two herring stocks due to the lower salinity Baltic Sea compared to Atlantic waters. Swimbladders of Baltic herring need to be bigger to achieve a certain degree of buoyancy. Morphological swimbladder dimensions of Baltic herring and sprat were found to be different. Herring had a significantly larger swimbladder height at a given length compared to sprat, resulting in a modelled TS that was on average 1.2 dB stronger. Water depth, and therefore the increase in ambient pressure, was found to have a considerable effect on the size and shape of the herring swimbladder. Modelled TS values were found to be around 3 dB weaker at a depth of 50 m compared to surface waters. At 200 m, this difference was estimated to be about 5 dB. The Bayesian model predicted mean abundances and biomass were 23 and 55% higher, respectively, than the ICES estimates. The discrepancy was linked to the depth-dependency of the TS model and the particular size-dependent bathymetric distribution of herring in the survey area.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:552490 |
| Date | January 2010 |
| Creators | Fässler, Sascha M. M. |
| Contributors | Brierley, Andrew; Fernandes, Paul G. |
| Publisher | University of St Andrews |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10023/1703 |
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