Targeted behavioural and microclimate data collection substantially improves biophysical model predictions of energy expenditure in wild birds
Data files
Jul 08, 2026 version files 58.06 KB
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03-05-2025_Conradie_Babblers_Baseline_models.R
3.73 KB
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14-02-2023_Conradie_Babblers_Scans.R
3.16 KB
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DEE_babblers_empirical.xlsx
11.08 KB
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EndoR_devel_customized.R
27.23 KB
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November_EndoR_babblers_chamber.R
6.25 KB
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README.md
6.61 KB
Abstract
Energy is fundamental for life, and accurately predicting animals’ energy expenditure under changing environments is central to ecology and global change biology. Energy expenditure in free-living animals can be directly quantified using the doubly labelled water (DLW) technique or estimated by extrapolating respirometric measurements of metabolic rates or modelled using biophysical principles. However, the accuracy of these latter approaches depends on data quality and model parameterisation. We evaluated respirometry extrapolations and biophysical models for estimating daily energy expenditure (DEE) in a free-living bird, the Southern Pied Babbler (Turdoides bicolor) against DLW measurements. Respirometry extrapolations and biophysical models were parameterised using: a) assumed animal behaviour and assumed operative temperature (Te), b) assumed animal behaviour and measured Te, c) fine scale (focal) observations of behaviour under natural conditions and measured Te, or d) coarse scale (scan) behaviour observations and measured Te. We then quantified the minimum field-collected data needed to accurately predict measured DEE across a range of daily maximum air temperatures (Tair). RScripts used to predict free ranging energy expenditure in birds, using these two approaches (biophysical models and respirometry) are provided here.
Dataset DOI: 10.5061/dryad.brv15dvr8
Description of the data and file structure
We provide a novel approach for predicting energy expenditure by incorporating detailed behaviour and microsite data for birds using a biophysical model and respirometry extrapolation. The model was parameterised using species-specific functional traits (see Conradie et al., 2023 for details) and published behavioural data (Bourne et al., 2020). Below we have provided the biophysical traits used in the parameterisation of the model, the customized endotherm model, and scripts for comparing model performance for predicting energy expenditure. We have noted below the sequence in which these scripts need to be run.
Variable descriptions:
| Variable | Description |
|---|---|
| SHAPE | Ellipsoid shape |
| TC (°C) | Target normothermic body temperature for optimal performance |
| TC_MAX (°C) | Maximum voluntary Tb before reaching potentially lethal Tb |
| TC_INC (°C) | Increments by which TC is increased |
| ANDENS (kg/m3) | Body density |
| DHAIR (m) | Feather diameter, set for ventral and dorsal sides respectively |
| LHAIR (m) | Feather length, set for ventral and dorsal sides respectively |
| ZFUR (m) | Plumage depth, set for ventral and dorsal sides respectively |
| RHO (1/m2) | Plumage density, set for ventral and dorsal sides respectively |
| REFL | Feather reflectivity (fractional, 0-1) |
| SAMODE | Bird skin surface area allometry from Walsberg & King (1978) |
| Q10 | Effect of Tb on metabolic rate when Tb > core Tb |
| QBasal (W) | Basal heat production |
| DELTAR (°C) | Respiratory heating of breath, when Tb > Tair. Otherwise, air leaving lunges was assumed to be the same as Tb. |
| EXTREF | O2 extraction efficiency (%)d |
| AK2 (W/mK) | Conductivity of fat |
| AK1 & AK1_MAX (W/mK) | Range of thermal conductivities of flesh. |
| PCTBAREVAP (%) | Surface area for evaporation that is skin (i.e., bare skin, where cutaneous evaporation can occur) |
| PCTWET (%) | Part of skin that acts as a free-water surface |
| PCTWET_MAX (%) | Maximum surface area acting as a free-water surface |
| PCTWET_INC (%) | Intervals by which skin wetness is increased |
| PANT_MAX | Maximum respiratory rate, defined as the multiplier on airflow through the lungs to simulate panting. Determined by metabolic rate. |
| PANT_MULT | Maximum possible increase in basal metabolic rate due to panting (i.e., 0.5 would result in 1.5 x BMR when panting is at the max value) |
| PANT_INC | Increment for a multiplier on breathing rate to simulate panting |
Code/software
The NicheMapR release relevant to this study (v3.0.0) and the endotherm component are both available via Zenodo (Kearney 2020).
Each data file or script is explained below:
EndoR_devel_customized.R - This script adjusts the endoR_devel (v3.0.0) model to represent the general behavioural and physiological requirements of a bird in response to skin temperature. Thereafter the adjusted endotherm model was run with species-specific biophysical traits (e.g., body dimensions) and physiological responses (e.g., base skin wetness) (see Conradie et al., 2023 Table S1 for values used).
03-05-2025_Conradie_Babblers_Baseline_models.R - This script shows the basic linear models fit to biophysical model predictions of energy exchange.
14-02-2023_Conradie_Babblers_Scans.R - This script was used to classify behaviours from observational studies, which were used to model energy expenditure. Behavioural data are available on request.
DEE_babblers_empirical.xlsx - The raw energy expenditure data derived from doubly labelled water experiments. Here Tmax is the maximum daytime air temperature (°C) and DEE is the measured daily energy expenditure.
November_EndoR_babblers_chamber.R - Species-specific parameterisation of the endoR_devel_customised script (see Conradie et al., 2023 Table S1 for values used).
Code/software
R is required to open the scripts and require the NicheMapR (v3.0.0) and microclima (Maclean et al., 2019; v2.0.0) packages to be installed.