Data from: A laser-equipped tunnel for the assessment of multiple burst swimming traits in fishes
Data files
Jan 02, 2024 version files 536.62 KB
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Burst_MS_Analysis_Data.csv
524.94 KB
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Burst_MS_Ucrit_Data.csv
2.34 KB
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README.md
9.33 KB
Abstract
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 (Oncorhynchus mykiss), 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 (R2 = 0.97). We also compared the burst performance of individual rainbow trout with their individual UCRIT, 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.
README: A laser-equipped tunnel for the assessment of multiple burst swimming traits in fishes
https://doi.org/10.25338/B8NH1X
This dataset contains multiple files associated with a manuscript entitled "A laser-equipped tunnel for the assessment of multiple burst swimming traits in fishes". This is a methods paper describing the development and use of a chamber used to precisely measure the burst performance of fishes. This burst tunnel uses an array of lasers to enable the calculation of burst velocity. The first step occurs on a Raspberry Pi which monitors the array of lasers and when in use records the time that each laser beam is broken. The Python script which does this is provided and named "Submission_Code_Lasers.py". The output of this Python script can then be interpreted with the included R script "TITLE".
We used this burst tunnel to evaluate the burst swim performance of Rainbow Trout and to use the burst tunnel to quantify novel metrics of burst performance and compare burst performance with sustained aerobic swim capacity (Ucrit). The data used in this analysis are contained in two included datasets, Burst_MS_Analysis_Data.csv which includes burst performance data for 30 fish across a suite of burst performance trials and Burst_MS_Ucrit_Data.csv, which includes the results of sustained aerobic swim performance (Ucrit) trials.
Finally, we have also included .drl files which can be used to recreate the printed circuit boards (PCBs) we designed to build the laser-detecting arrays for the burst tunnel.
Description of the data and file structure
Burst_MS_Analysis_Data.csv
This dataset contains all the individual burst events for the two rounds of delayed burst and the two rounds of exhaustive burst trials.
Column Names
TRIAL_TYPE: This column identifies the data as arising from a burst trial using the 'DELAYED' or 'EXHAUSTIVE' protocol.
PIT: A unique code for each fish based upon its implanted Passive Integrated Transponder (PIT) tag.
ORIENTATION: Whether the burst event on the right (R) or left (L) of the burst tunnel
FISH_ID: A unique fish ID that contains a fish's PIT tag number, the trial ID and the date (PIT_TrialID_Date)
TRIAL_NUMBER: Identifies whether the trial represented the first (1) or second (2) delayed or exhaustive burst protocol.
DATE: Date of the trial (mm.dd.yyyy)
BURST_COUNT: An integer value describing what number burst event it is for each fish
START_TIME: number if seconds between the initiation of the script and the specific burst event. 573 933 1404 1682 1765 ...
MASS: The fish's mass in grams.
SL: The fish's standard length in centimeters
FL: The fish's fork length in centimeters: NA indicates data not recorded
TL: The fish's total length in centimeters
STATUS: Identifies burst events wherein the fish refused to burst (R2B, n = 4) or where there was a user error that invalidated the trial (ERROR, n = 14). NA indicates that burst event occurred without error.
PEAK_SPEED: The burst velocity of the fastest segment (body lengths per second). NA indicates velocity could not be calculated due to error'd STATUS.
PEAK_SPEED_1_1H: The burst velocity of the fastest segment in the first half of the burst tunnel (body lengths per second). NA indicates velocity could not be calculated due to error'd STATUS.
PEAK_SPEED_2: The second fastest burst segment velocity (body lengths per second). NA indicates velocity could not be calculated due to error'd STATUS.
PEAK_SPEED_2_1H: The second fastest burst segment velocity in the first half of the burst tunnel (body lengths per second). NA indicates velocity could not be calculated due to error'd STATUS.
PEAK_SPEED_3: The third fastest burst segment velocity (body lengths per second). NA indicates velocity could not be calculated due to error'd STATUS.
PEAK_SPEED_3_1H: The third fastest burst segment velocity in the first half of the burst tunnel (body lengths per second). NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_TOP_3: The average velocity of the first, second, and third fastest segments. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_TOP_2_4: The average velocity of the second, third, and fourth fastest segments. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_TOP_3_5: The average velocity of the third, fourth, and fifth fastest segments. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_TOP_10P: The average velocity of all segments in the top 10 percent of segments. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_1H: The average velocity of all segments in the 1st half of the tunnel. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_1Q: The average velocity of all segments in the 1st quarter of the tunnel. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED_2Q: The average velocity of all segments in the 2nd quarter of the tunnel. NA indicates velocity could not be calculated due to error'd STATUS.
AVE_SPEED: The average velocity of all segments in the burst tunnel. NA indicates velocity could not be calculated due to error'd STATUS.
Burst_MS_Ucrit_Data.csv
This dataset contains the maximum sustained aerobic swim speed (Ucrit) of 30 individual rainbow trout. There are no missing values.
Column Names
FISH_ID: A unique fish ID that contains a fish's PIT tag number, the trial id, tunnel used and the date (PIT_TrialID.Tunnel_Date)
PIT: A unique code for each fish based upon its implanted Passive Integrated Transponder (PIT) tag.
TUNNEL_ID: Ucrits were conducted in twin 30L Loligo swim tunnels. They are identified as either '1' or '2'.
DATE: Date of the trial (mm.dd.yyyy)
MASS: The fish's mass in grams.
SL: The fish's standard length in centimeters.
UCRIT: The maximum sustained swim speed of each Rainbow Trout reported in Body lengths per second. The fish's standard length (SL) was used to calculate this size-corrected velocity.
TRIAL_TYPE: The only value in this column is 'UCRIT' to distinguish it from the two types of burst trials contained in the Burst_MS_Analysis_Data.csv.
Detector_Boards-PTH.drl and Detector_Boards-NPTH.drl
These two files are drill (.drl) files for the fabrication of the custom printed circuit boards (PCBs) used to hold the light detectors that constitute the laser-gate array. These files can be used to recreate the same form of PCBs. Graphical illustrations of these drill files are located in the Supplementary materials of the associated manuscript.
Code/Software
Submission_Code_Lasers.py
This is a Python script that can be run on a Raspberry Pi attached to the burst tunnel described in the present manuscript. This script will allow the user to run a burst swim performance trial. It will ask the user to enter details about the trial, enable the user to conduct test to ensure the burst tunnel is being properly interpreted and all the laser-gates are functioning. This script uses the GPIO pins on the Raspberry Pi to monitor up to 25 laser-gates. During a trial, when a laser-gate is obscured by a fish, the change in voltage at the GPIO pin induces the script to record the time of that voltage change. At the end of a burst event, a vector of timings of when each laser-gate was interrupted is stored in an output file. When the user elects to end the script (wishes to not conduct any further burst events) then the script saves the output file which can be analyzed in the R script entitled TITLE.
This script requires three packages. 'RPi.GPIO', 'time' and 're'
For a more detailed and graphical description of how the code works, see Figure 2 in the manuscript.
Zillig_Burst_Processor_1.r
This is an R script which takes the timing output produced from the Submission_Code_Lasers.py and uses a Burst Tunnel Spec dataframe to compute measures of fish velocity. This code is mean to process several burst experiments at once. It will create directories for each fish and each single burst event. It will ultimately produce a large dataframe with calculated metrics (e.g., Peak Speed, Average Speed, etc.), plots of the burst performance, measures of experiment success (e.g., any gates missed? any errors?) which can then be analyzed to evaluate the burt performances of fish.
The Burst Tunnel Spec dataframe should have two columns (see chunk below to generate the generic specs used in the study). The GATE_ID identifies each individual laser beam, and the GATE_DISTANCES indicates at what distances from the start (left) each laser is. A study using a tunnel with a different number or arrangement of lasers would need a modified tunnel.specs dataframe to accurately measure velocity.
tunnel.specs <- data.frame("GATE_ID" = seq(0:24), # One gate ID per laser beam. Our code used 25 labeled 0 to 24
"GATE_DISTANCES" = c(0,1,2,4,6,10,15,20,25,35,40,45,50,55,60,65,70,75,80,84,86,88,89,90) # these are the distances (cm) used in the present study, they would be case dependent on the layout of the lasers.
)
This code requires the following packages.
library(ggplot2)
library(plyr)
library(dplyr)
library(reshape2)
library(tidyverse)
library(Rfast)
library(lubridate)
Methods
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.