Data from: Dehydrated snakes reduce postprandial thermophily

Azzolini, Jill 1 ; Roderick, Travis1; DeNardo, Dale1

Research facility: Arizona State University

Published Apr 09, 2026 on Dryad. https://doi.org/10.5061/dryad.1vhhmgqzv

Data files

Apr 09, 2026 version files 10.59 KB

data.csv

8 KB
README.md

2.59 KB

Abstract

Transient thermophily in ectothermic animals is a common response during substantial physiological events. For example, ectotherms often elevate body temperature after ingesting a meal. In particular, the increase in metabolism during the postprandial response of pythons - known as specific dynamic action – is supported by a concurrent increase in preferred temperature. The objective of this study was to determine whether hydration state influenced digestion-related behavioral thermophily. Sixteen (8 male and 8 female) Children’s pythons (Antaresia childreni) with surgically implanted temperature data loggers were housed individually and provided a thermal gradient of 25-45 °C. Body temperature was recorded hourly beginning 6 days prior to feeding and for 18 days post-feeding, thus covering pre-feeding, postprandial, and post-absorptive stages. Each snake underwent this 24-day trial twice, once when hydrated and once when dehydrated. Our results revealed a significant interaction between temperature preference, digestive stage, and hydration state. Under both hydrated and dehydrated conditions, snakes similarly increased their body temperature shortly after consuming a meal, but during the later days of the postprandial stage, snakes selected significantly lower (~1.5°C) body temperature when they were dehydrated compared to when they were hydrated. Our results demonstrate a significant effect of hydration state on postprandial thermophily, but the impact of this dehydration-induced temperature reduction on digestive physiology (e.g., passage time, energy assimilation) is unknown and warrants further study.

https://doi.org/10.5061/dryad.1vhhmgqzv

Description of the data and file structure

The objective of this study was to determine whether hydration state influenced digestion-related behavioral thermophily. Sixteen (8 male and 8 female) Children’s pythons (Antaresia childreni) with surgically implanted temperature data loggers were provided a thermal gradient of 25-45 °C. Body temperature was recorded hourly beginning 6 days prior to feeding and for 18 days post-feeding. Each snake underwent this 24-day trial twice, once when hydrated and once when dehydrated.

Files and variables

File: data.csv

Description: The dataset which was imported into R for statistical analysis.

Variables

Snake ID: The ID number of each snake in the study.
Group: G1 (experienced hydrated meal then dehydrated meal) and G2 (experienced dehydrated meal then hydrated meal)
State: PF (pre-feeding), PP 1-4 (post prandial days 1-4), PP 5-8 (post prandial days 5-8), PP 9-12 (post prandial days 9-12), and PA (post absorptive)
H or D: Hydrated or Dehydrated
Avg.temp: Average recorded body temperature in degrees Celsius (℃) for each feeding period.
s.e.: standard error of average body temperature
SVL: Snout-vent length in millimeters (mm).
Initial mass: The mass of the snake in grams (g) at the beginning of the study
Sex: Male or Female
BCI: Body Condition Index: calculated by taking the residuals of SVL vs. mass

Statement regarding cells filled with "NA"

Two male snakes (Snake IDs M10 and M34) refused to eat the mouse offered to them for the dehydrated feeding cycle. Thus, we do not have data for either snakes' body temperatures for the following time points during their dehydrated feeding cycle: Post-Prandial Days 1-4, Post-Prandial Days 5-8, Post-Prandial Days 9-12, and Post-Absorptive. We included the dehydrated Pre-Feeding body temperature for both snakes as this data was not affected by whether the snakes ate. We included the results for both snakes for all timepoints during the hydrated feeding cycle since both snakes ate the mouse that was offered.

Code/software

We used R version 4.2.0 to analyze the data contained within our file titled "data.csv". We tested multiple ANOVA models before choosing the one that was the best fit for the data. We then checked that all assumptions were met and performed post-hoc testing by using Tukey's test. Code is uploaded as 'Software' to Zenodo.