Data from: A laser-equipped tunnel for the assessment of multiple burst swimming traits in fishes

Abstract

<p>Burst swimming performance in fishes is relatively understudied despite its critical role in predation attempts and prey evasion, spawning events, and passing hydraulic challenges. Burst swimming is characterized by fast acceleration, over a short distance and of limited duration. The bulk of fast-start performance research uses analysis of high-speed recordings of fish behavior. While behavioral video analysis has improved, it is still expensive in both processing time and computational resources. Here we introduce a laser-gated burst tunnel that improved upon past designs by introducing an adjustable number of lasers (≤ 25) that facilitated greater resolution on burst performance as well as novel laser arrangements facilitating novel performance metrics (e.g., fatigue rate, burst capacity). We quantified the burst velocity, burst capacity and fatigue rate of rainbow trout (<em>Oncorhynchus mykiss</em>), a widely distributed and studied species. We directly compared the results measured by our device to simultaneously collected high-speed camera data and find the velocity estimates to be highly accurate (R<sup>2</sup><sub> </sub>= 0.97). We also compared the burst performance of individual rainbow trout with their individual U<sub>CRIT,</sub> a commonly measured metric of aerobic swimming performance. We found little correlation between the two traits, indicating that fish capable of rapid burst swimming are not necessarily fast sustained swimmers. Finally, we defined and quantified two novel traits of burst swimming performance: burst capacity (the number of burst events that can be elicited prior to performance decline), and fatigue rate (the rate of decline associated with repeated bursting). The burst tunnel is an adjustable platform for quantifying understudied elements of fish swimming physiology, improving design of fish passage technology, and facilitating discoveries in how burst swimming performance changes with environmental conditions.</p><p>Funding provided by: University of California, Agricultural Experiment Station*<br>Crossref Funder Registry ID: <br>Award Number: 2098-H</p><p>Funding provided by: California Sea Grant<br>Crossref Funder Registry ID: https://ror.org/02yn1nr06<br>Award Number: 19054</p><p>Funding provided by: State Water Resources Control Board<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100004814<br>Award Number: 20-036-300</p><p>This dataset was collected using a novel burst swimming performance tunnel. The Python script which controls the Raspberry Pi and collects data from the burst tunnel is included. Also included is the processing R script which converts the output of the Raspberry Pi into velocity measurements for each fish's burst performance. </p&gt