Data from: Crush-resistant air-sacs allow insect larvae to exploit aquatic habitat at extreme depth
Data files
Jun 05, 2026 version files 12.95 GB
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AZFP_Data.zip
12.91 GB
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Data_S1_Lakefly_layers.csv
16.98 KB
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Data_S2_fish_tracks.csv
34.28 MB
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Data_S3_Water_quality_sonde.csv
33.91 KB
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Data_S4_Chaoborus_depth_of_failure.csv
6.42 KB
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Data_S5_Airsac_stiffness_and_compression_to_failure.csv
35.43 KB
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Data_S6_C_edulis_airsac_work_pH_steps.csv
3.38 KB
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Data_S7_C_edulis_airsac_work_pressure.csv
11.77 KB
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Data_S8_C_edulis_airsac_unpressurized_pH-series.csv
867 B
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README.md
15.75 KB
Abstract
The absence of aquatic insects from pelagic marine habitats has been attributed to their air-filled tracheal respiratory system, which could implode at depth while undertaking diel vertical migrations (DVMs) to escape predatory fish. However, we found that the aquatic larvae of the lake fly Chaoborus edulis in Lake Malawi have modified their tracheal system into reinforced buoyancy-regulating air-filled sacs, allowing them to escape predatory fish by undertaking DVMs well over 200 m deep into the lake’s anoxic hypolimnion during the day. The crush-depth of their air-sacs increases with each instar, with final instars resisting implosion at depths greater than half a kilometer. Contrary to expectations, these insects adapt to the extreme hydrostatic pressures of deep-water habitats, allowing them to co-exist with pelagic fish.
Chaoborus edulis larval layers detected in Lake Malawi using a moored active acoustic instrument in August 2023
File: Access Data_S1_Lakefly_layers.csv
This is a CSV (comma separated values) file describing layers of Chaoborus edulis larvae detected using an Acoustic Zooplankton and Fish Profiler (AZFP; ASL Environmental, B.C., Canada) moored at 275 m depth in Nhkata Bay, Lake Malawi from the 12th to the 25th of August 2023.
Description of the data and file structure
The layers were detected, extracted and described in Python using the packages Echopype (Lee et al., 2021), echopy (Ariza et al., n.d.), and the Sound Scattering Layer Extraction Method (SSLEM; Proud et al., 2015).
Continuous layers were split according to change point detection in the vertical speed of the layer and each part was classified as "ascending", "descending" or "static".
Each row of this data file represents a section of a C. edulis layer after change point detection. Each layer is described by the following metrics. All depths and thicknesses are in metres (m). Dates are in the format DD/MM/YYYY.
- label: A unique numeric identifier
- shallowest_depth: The shallowest depth of the detected layer.
- mean_start_depth: The mean depth of the layer when first detected.
- deepest_depth: The deepest depth of the detected layer.
- min_depth_start: The minimum depth of the layer when first detected.
- max_depth_start: The maximum depth of the layer when first detected.
- min_depth_end: The minimum depth of the layer when last detected.
- max_depth_end: The maximum depth of the layer when last detected.
- mean_end_depth: The mean depth of the layer when last detected.
- mean_depth: The overall mean depth of the layer.
- std_mean_depth: The standard deviation of the mean depth of the layer.
- weighted_mean_depth: The mean depth of the layer weighted by the echo intensity (mean volume backscattering strength in dB re 1 m^-1).
- max_thickness: The maximum thickness of the layer at any point.
- min_thickness: The minimum thickess of the layer at any point.
- mean_thickness: The overall mean thickness of the layer.
- std_thickness: The standard deviation of the mean thickness of the layer.
- length_pings: The number of pings (one ping every 5 seconds) that the layer lasts for.
- start_time: The local time at the first detection of the layer.
- end_time: The local time at the last detection of the layer.
- wmd_start: The weighted mean depth (as 12) of the layer when first detected.
- wmd_end: The weighted mean depth fo the layer (as 12) when last detected.
- date: A number between 12 and 25 representing the calendar date in August in which the layer was detected.
- Acceleration: The weighted mean depth at the end of the layer (21) minus the weighted mean depth at the start of the layer (20), divided by the duration of the layer in seconds, to give acceleration in metres per second.
- DVM: Stage of diel vertical migration as "Descent", "Static", "Ascent 1", or "Ascent 2" based on visual examination.
- DVM2: Stage of diel vertical migration simplified to "Descent", "Static", or "Ascent".
Sharing/Access information
The raw data used for this analysis can be found on Dryad in the folder AZFP_Data.zip. The data files are in the .01A file format, a proprietary raw binary data format generated by the Acoustic Zooplankton Fish Profiler (AZFP), an echosounder built by ASL Environmental Sciences.
Code/Software
An example of the code used for SSLEM (layer detection and extraction) can be found here.
References
Ariza, A. et al. (no date) ‘echopy’. Available at: https://github.com/open-ocean-sounding/echopy (Accessed: 23 November 2023).
Lee, W.-J. et al. (2021) ‘Echopype: A Python library for interoperable and scalable processing of water column sonar data for biological information’. arXiv. Available at: https://doi.org/10.48550/arXiv.2111.00187.
Proud, R. et al. (2015) ‘A method for identifying Sound Scattering Layers and extracting key characteristics’, Methods in Ecology and Evolution, 6(10), pp. 1190–1198. Available at: https://doi.org/10.1111/2041-210X.12396.
Fish tracks detected in Lake Malawi using a moored active acoustic instrument in August 2023
File: Access Data_S2_fish_tracks.csv
This is a CSV (comma separated values) file describing fish tracks detected using an Acoustic Zooplankton and Fish Profiler (AZFP; ASL Environmental, B.C., Canada) moored at 275 m depth in Nkhata Bay, Lake Malawi from the 12th to the 25th of August 2023.
Description of the data and file structure
The tracks were detected in Echoview version 15 using the single target detection algorithm and the fish tracking algorithm. Times are local times.
Each row of this data file represents a single fish track.
- Region_ID: A unique numeric identifier
- Date_S: The date at the start of the fish track
- Time_S: The time at the start of the fish track.
- Date_E: The date at the end of the fish track.
- Time_E: The time at the end of the fish track.
- Date_M: The date at the mid-point of the fish track.
- Time_M: The time at the mid-point of the fish track.
- Num_targets: The number of detected single targets within the fish track.
- TS_mean: The mean target strength (dB re 1 m^2) of the fish track.
- TS_max: The maximum target strength (dB re m^2) of the fish track.
- TS_min: The minimum target strength (dB re m^2) of the fish track.
- Target_range_mean: The mean range of the fish track from the transducer (m).
- Target_range_max: The maximum range of the fish track from the transducer (m).
- Target_range_min: The minimum range of the fish track from the transducer (m).
- Fish_track_change_in_range: The change in range of the fish track (m).
- Time_in_beam: The time the fish track is within the echosounder beam (seconds).
Sharing/Access information
N/A
Code/Software
The data were processed in Echoview version 15.1.
References
Echoview Software Pty Ltd, 2025. Echoview®. Hobart, Australia.
250 m deep vertical water quality transect recorded using YSI EXO1 sonde in Lake Malawi
File: Data_S3_Water_quality_sonde.csv
This is a CSV (comma separated values) file describing the time course of a 250 m deep vertical water quality profile taken on 14 August 2023 (coordinates S11° 36.056' E34° 18.751') in the vicinity of the deployed Acoustic Zooplankton and Fish Profiler moored at 275 m depth in Nkhata Bay, Lake Malawi. A YSI EXO1 sonde was used employed equipped with the following sensors: Depth, Temp/Conductivity, pH/ORP, Total Algae, and Dissolved O2. All sensors were calibrated using traceable standards on July 26, 2025. The pH sensor was calibrated with NIST traceable pH 7 and pH 10 standard buffers immediately before deployment in the field. All sensors were set to automatically record to the sonde's internal memory at 0.1 Hz. The sonde was lowered in 10-m increments on a 250 m long, pre-marked rope, held at each 10 m depth increment for 60 s, before being lowered a further 10 m.
Sonde configuration
For each variable measured by the YSI EXO1 sonde, the traceable serial number of the YSI sensor employed in its measurement is provided below as (variable: sensor serial number)
Chlorophyll RFU: 17F103628
Cond µS/cm: 15H102553
Depth m: 13F100495
nLF Cond µS/cm: 15H102553
ODO % sat: 13J101191
ODO % local: 13J101191
ODO mg/L: 13J101191
ORP mV: 22M100040
Pressure psi a: 13F100495
Sal psu: 15H102553
SpCond µS/cm: 15H102553
TAL PC RFU: 17F103628
pH: 22M100040
pH mV: 22M100040
Temp °C: 15H102553
Vertical Pos. m: 13F100495
Battery V: 13J101649
Cable Pwr V: 13J101649
Description of the data and file structure
Each column of this data file records the date, time or instrument parameter recorded during the incremental lowering and raising of the EXO1 water quality sonde in Lake Malawi.
Column Heading:
- Date (MM/DD/YYYY): Calendar day of recorded parameters formatted as Month, Day, Year
- Time (HH:mm:ss): Time at which parameter recorded, formatted as 24 h local time in Malawi: hours, minutes, seconds.
- Site Name: Location name where sonde deployed for recording
- Chlorophyll RFU: Relative Fluorescence Output from EXO Total Algae Phycocyanin Digital Smart Sensor - Freshwater (Measures and outputs fluorescence measured from both chlorophyll & blue-green algae)
- Cond µS/cm: Conductivity measured in microsiemens per centimeter
- Depth m: Depth in meters calculated from hydrostatic pressure
- nLF Cond µS/cm: Temperature corrected conductivity calculated from conductivity and temperature using a non-linear function (nLF)
- ODO % sat: % saturation of oxygen in water relative to normoxia at 1 atm, measured using fluorescent oxygen sensor (Optical Dissolved Oxygen; ODO)
- ODO % local: % saturation of oxygen in water relative to normoxia at local barometric pressure, measured using fluorescent oxygen sensor (Optical Dissolved Oxygen; ODO)
- ODO mg/L: Concentration of of oxygen in water, calculated using ODO % sat, correcting for temperature and salinity
- ORP mV: Oxidation-Reduction Potential measured in millivolts
- Pressure psi a: Absolute local hydrostatic pressure in pounds per square inch recorded using the sonde's integrated pressure transducer
- Sal psu: Salinity in practical salinity units (PSU) calculated from conductivity
- SpCond µS/cm: Specific Conductance calculated from normalizing conductivity (Cond µS/cm) to 25°C
- TAL PC RFU: Total Algae-Phycocyanin measured in relative fluorescence units
- pH: pH calculated from mV potential measured using pH electrode
- pH mV: Raw potential measured using pH electrode in millivolts
- Temp °C: Water temperature in Celcius measured using thermistor temperature sensor
- Vertical Position m: Real-time vertical position calculated from depth sensor (unaveraged)
- Battery V: Sonde's internal battery voltage
- Cable power: Voltage of connected hand-held logger
Chaoborus air-sac experimental data
The .csv formatted data files Data_S4 to Data_S8 describe the results of experiments on the air-filled hydrostatic organs (air-sacs) of Chaoborus midge larvae. Air-sacs were either subjected to hydrostatic pressure or changes in pH, causing a change in size. Changes in the profile area of an air-sac are directly proportional to changes in its length. Each .csv file corresponds to the data of a particular figure in the publication.
Description of the data and file structure
File: Data_S4_Chaoborus_depth_of_failure.csv
Description: corresponding to Figure 2. Intact larvae and pupae subjected to hydrostatic pressure until air-sac or respiratory horn failure, respectively.
Column headings:
- species: Gives the abbreviated Latin name of the species on which the observation was made
- instar # or pupa: refers to the stage of development, including the pupal stage for some observations
- depth.m: gives the simulated depth in meters of fresh water which caused an air-sac or respiratory horn to fail, and is equivalent to Pa / (fresh water density × gravitational acceleration)
- ID: a unique identifier for each larva
File: Data_S5_Airsac_stiffness_and_compression_to_failure.csv Description: corresponding to Figure 3. Changes in excised air-sac dimensions with increasing hydrostatic pressure.
Column headings:
- species: Gives the abbreviated Latin name of the species on which the observation was made
- larva: individual larva identity
- Area: the area in mm2 of the air-sac profile
- sac: refers to a particular air-sac which a given larva has contributed to the experiment (there usually being more than one)
- type: indicates whether the air-sac is one of the larva’s anterior or posterior air-sacs
- psi: pressure applied in lbs per square inch
File: Data_S6_C_edulis_airsac_work_pH_steps.csv
Description: corresponding to Figure 4A (left hand side). Air-sacs under no hydrostatic pressure subjected to an increase in pH.
Column headings:
- larva: individual larva identity
- Area: the area in mm2 of the air-sac profile
- sac: refers to a particular air-sac which a given larva has contributed to the experiment
- type: indicates whether the air-sac is one of the larva’s anterior or posterior air-sacs
- pH: the pH to which each air-sac was equilibrated
- pH6_length: the length in mm of each air-sac when measured at pH 6
- pH6_width: the width in mm of each air-sac
File: Data_S7_C_edulis_airsac_work_pressure.csv
Description: corresponding to Figure 4A (right hand side). Air-sacs at a constant pH of 8 subjected to an increase in hydrostatic pressure.
Column headings:
- larva: individual larva identity
- Area: the area in mm2 of the air-sac profile
- sac: refers to a particular air-sac which a given larva has contributed to the experiment
- type: indicates whether the air-sac is one of the larva’s anterior or posterior air-sacs
- pH6_length: the length in mm of each air-sac when measured at pH 6
- pH6_width: the width in mm of each air-sac when measured at pH 6
- psi: pressure applied in lbs per square inch
The data in files Data_S6 and Data_S7 pertain to the same cohort of air-sacs and represent a two part experiment. First, the air-sacs were subjected to an increase in pH, causing them to expand, then they were subjected to an increase in hydrostatic pressure, causing them to contract.
File: Data_S8_C_edulis_airsac_unpressurized_pH-series.csv
Description: corresponding to Figure 4B. Changes in the profile cross-sectional area (mm2) of air sacs equilibrated to a pH series from 2 to 10.
Column headings:
- larva: individual larva identity
- pH 2: pH to which the air-sac was equilibrated
- pH 3: pH to which the air-sac was equilibrated
- pH 4: pH to which the air-sac was equilibrated
- pH 5: pH to which the air-sac was equilibrated
- pH 6: pH to which the air-sac was equilibrated
- pH 7: pH to which the air-sac was equilibrated
- pH 8: pH to which the air-sac was equilibrated
- pH 9: pH to which the air-sac was equilibrated
- pH 10: pH to which the air-sac was equilibrated
- diam_mm: air-sac diameter in millimeters
AZFP raw sonar data
The file AZFP_Data.zip contains the raw data collected by ASL Environmental Sciences' AZFP (Acoustic Zooplankton Fish Profiler) echosounder during its 13-day deployment in Lake Malawi near Nkhata Bay, Malawi. The AZFP was deployed on the 12th August, 2023, at 1:30 pm at 11° 36' 3.474" S 34° 18' 43.2828" E, with the transducers facing upward at a depth of 275 m. It was recovered on August 25th at 1:30 pm for a total deployment duration of 13 days. The recorded raw binary sonar data is in the proprietary .01A format generated by the AZFP echosounder. An associated .xml file contains the metadata (transducer frequencies, sampling parameters, etc.) needed to unpack the binary .01A files. Each .01A file name is an 8-digit numeric string with two digits representing the year, month, day and hour in 24h format (i.e., YYMMDDHH). The .01A files can be read using the open-source Python package Echopype found here: https://echopype.readthedocs.io. Echopype provides comprehensive instructions for converting the raw .01A files into netCDF or Zarr formats, as well as instructions for working with and visualizing the sonar data.
