DOI: 10.5281/zenodo.11655204
title: Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates

description: Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).

Abstract

Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, Linepithema humile. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.

 



VolEst_C1_volume_calculation_multiprocessing.py: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.

VolEst_C2_interactive_GUI.py: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.

VolEst_C3_linear_regression.py: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.

VolEst_C4_statistical_analysis: Complete statistical analysis and code for the manuscript.

VolEst_D1_sucrose_density.csv: Data obtained to quantify the density of sucrose solutions of varying molarity.

VolEst_D2_accuracy_weight_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.

VolEst_D3_accuracy_weight.zip: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.

VolEst_D4_accuracy_nanoliter_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.

VolEst_D5_accuracy_nanoliter.zip: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.

VolEst_D6_sucrose_caffeine_consumption_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.

VolEst_D7_sucrose_caffeine_consumption.zip: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.

VolEst_Camera_A-Henrique-2023-09-20.zip: DeepLabCut labels and trained network for camera A.

VolEst_Camera_B-Henrique-2023-09-20.zip: DeepLabCut labels and trained network for camera B.

VolEst_base.stl: 3D file for the resin platform used in the experimental validation of the setup.

VolEst_platform.stl: 3D file for the resin platform used in the experimental validation of the setup.