Date of Award

Summer 8-18-2017

Level of Access

Open-Access Thesis

Degree Name

Master of Science (MS)




Gayle Zydlewski

Second Committee Member

Damian Brady

Third Committee Member

Neal Pettigrew


Spatiotemporal advantages linked to hydroacoustic sampling techniques have caused a surge in the use of these techniques for fisheries monitoring studies applied over long periods of time in marine systems. Dynamic physical conditions such as tidal height, boat traffic, floating debris, and suspended particle concentrations result in unwanted noise signatures that vary in intensity and location within a hydroacoustic beam over time and can be mixed with the acoustic returns from intended targets (e.g., fish). Typical processing filters applied over long term datasets to minimize noise and maximize signals do not address spatiotemporal fluctuations of noise in dynamic systems. We present a methodological approach to obtain fish counts from large hydroacoustic datasets collected in dynamic systems by 1) developing an automated processing algorithm that imposes spatially and temporally varying noise thresholds according to the signal-to-noise ratio present, 2) creating a fish count index standardized to the noise conditions present at the time of detection, and 3) validating the applied algorithm by manually quantifying the margins of error of automated fish counts from the processing algorithm. We demonstrate the efficacy of this method by applying it to a six-month hydroacoustic dataset collected in the tidal region of the Penobscot River, Maine USA. It enabled us to recover 60% of the data that would otherwise have been lost due to noise contamination. The successful implementation of this method allows for datasets with varying signal-to-noise ratios to be standardized based on the noise signature present, enabling researchers to maximize their data usage.

Quantifying how fish abundance changes after a significant portion of their natural habitat becomes re-accessible is critical to gauge the success of large restoration efforts. Because fish abundance also changes with naturally fluctuating environmental conditions, examining abundance relative to these conditions can indicate fish responses to both anthropogenic and natural river variation. A side-looking hydroacoustic system was used to estimate fish abundance in the Penobscot River, ME from 2010-2016, where 2010-2013 were pre-dam removal conditions, and 2014-2016 were post-dam removal conditions. The river was monitored during non-ice condition periods, roughly May to November annually. Automated data processing enabled continuous abundance estimates from fish tracks. A fourfold increase in median fish abundance occurred in the fall compared to spring and summer, regardless of dam presence. Concurrent with restoration activities, fish abundance increased approximately twofold pre- to post-dam removal. We examined the influence of natural environmental conditions including tide, discharge, temperature, diurnal cycle, day length, moon phase, as well as restoration activities (focusing on dam presence) on variability in fish abundance. Day length (or photoperiod) was the most important predictor variable in all eight time-series analyzed. During the fall migration, abundance was generally higher during outgoing tides, at night, and during relatively high river discharge. In the early fall, when daylength was between 11.28 h and 12 h (September 24th to October 6th) and water temperature was above 11.96 °C, an eightfold increase in fish abundance was recorded in post-dam removal years. Alewife stocking numbers increased post-dam removal relative to pre-dam removal years and likely contributed to the increased fish abundance. This is one of the first validated tools to continuously examine the response of fish abundance to a major river restoration activity. In this application, it significantly increased our understanding of how fish abundance changed in the Penobscot River as result of major restoration efforts and provides a basic understanding of fish responses to naturally fluctuating environmental conditions.