Metabolic traits are shaped by phylogenetic conservatism and environment, not just body size
Data files
Jul 30, 2025 version files 1.07 MB
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metadata_mr_ant.csv
3.40 KB
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mr_ant_525.csv
1.03 MB
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mr_ant_climate_modelling.zip
25.68 KB
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mr_genus_tree.tre
1.25 KB
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mr_phylo.tre
6.70 KB
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README.md
5.65 KB
Abstract
Metabolic rate dictates life’s tempo, yet how ecological and environmental factors integrate to shape metabolic traits remains contentious. Considering metabolic traits of 114 species of ants from seven subfamily clades along a 1,500 km climatic and soil phosphorus availability gradient in Australia, we tested four hypotheses relating to variation in metabolic rate due to niche conservatism, temperature, aridity, and ecological stoichiometry. We also tested the contested hygric hypothesis, which predicts that insect ventilation patterns can be modified to reduce water loss in arid environments. Mass-independent metabolic rate was phylogenetically conserved. The ant clade Myrmecia had metabolic rates 3 to 10x higher than other species, likely related to their large eye size, a correlate of cognitive complexity. Metabolic rate was higher in ants from warm, arid sites relative to those from wet, cool sites. A weak positive interaction between soil phosphorus and body mass indicated that, at sites with low soil phosphorus, smaller ants respired at higher rates than expected based on their mass—consistent with ecological stoichiometry theory. Larger ants, regardless of clade, were more likely to exhibit discontinuous gas exchange (DGC) with increasing aridity, likely reflecting a water conservation strategy. Phylogenetic conservatism of metabolic rate and a moderate influence of environment suggest that, in addition to biophysical geometric constraints, metabolic rate has evolved to match the energetic demands required of ecological strategies to address environmental stressors. For larger insect species confronting their metabolic limits, DGC may promote resilience in a world that is becoming hotter and more arid.
https://doi.org/10.5061/dryad.d7wm37qb7
Description of the data and file structure
See the methods section for details on metabolic data collection and analysis.
Files:
mr_ant_525.csv: Main data CSV containing metabolic rate data and ventilation patterns for individual ants collected from six environmental locations, including climatic and soil P variables and activity data
metadata_mr_ant.csv: A list of the column headings from mr_ant_525.csv with the metadata and detailed descriptions, units, and NA value indications for each parameter and trait
mr_ant_models.R: An annotated R script with the data wrangling and main analysis for the findings presented in the manuscript
mr_ant_supp_analysis.R: An annotated R script with supplementary tests to support findings from the main models
mr_genus_tree.tre: A genus phylogenetic tree of 34 ant genera used in the study
mr_phylo.tre: A site-species level tree of 139 site-species (where species at sites (n = 11) are tips) containing soft polytomies built off a genus level tree.
mr_ant_climate_modelling.zip: A folder containing annotated R scripts and data files to create interpolated climate variables for sites at the macroscale (from worldclim 2.1 - not used in main analysis) and microscale (microclimate models of temperature and vapor pressure deficit).
Files and variables
File: metadata_mr_ant.csv
Description: Metadata for mr_ant_525.csv
File: mr_genus_tree.tre
Description: A genus-level phylogeny of ant genera used in the study. Phylogenetic tree modified from Economo et al. (2018). Macroecology and macroevolution of the latitudinal diversity gradient in ants. Nature Communications. 9:1-8.
File: mr_phylo.tre
Description: A species-level phylogeny of ant "species" used in the study, where tips are soft polytomies with species at sites as separate tips. Phylogenetic tree modified from Economo et al. (2018). Macroecology and macroevolution of the latitudinal diversity gradient in ants. Nature Communications. 9:1-8.
File: mr_ant_525.csv
Description: Main data text file. See the metadata CSV for descriptions of variables.
Folder: mr_ant_climate_modelling.zip
Description: A folder containing annotated R scripts and data files to create interpolated climate variables for sites at the macroscale (from worldclim 2.1 - not used in main analysis) and microscale (microclimate models of temperature and vapor pressure deficit).
Zipped Folder Files Metadata:
climate_modelling.R – R script to run analyses for obtaining microclimate data layers used in the main analysis. Includes link and details on downloading Worldclim 2.1 files for producing the macroclimate layers.
metadata_mr_ant_climate_modelling - Metadata for mr_ant_climate_modelling.zip
macroclim_mean_annual.csv - Interpolated climate variables at macroscale using worldclim 2.1
Variables:
- site: location name abbreviation matches to ‘location2’ column in mr_ant_525.csv
- site.pcat: location name plus site phosphorus category (HP = high phosphorus, LP = low phosphorus)
- long: longitude
- lat: latitude
- mat = Mean annual macroclimate temperature (Worldclim 2.1) (°C) Bio1 30s
- map = Mean annual macroclimate precipitation (Worldclim 2.1) (mm) Bio12 30s
microclim_2009_2023_annual_means.csv - output from climate_modelling.R analysis
Variables:
- id: site ID identifier - output from microclimate model
- mat.micro: Mean annual microclimate temperature per site (°C)
- mat.rhmicro: Mean annual microclimate relative humidity (%)
- site.pcat: location name plus site phosphorus category (HP = high phosphorus, LP = low phosphorus)
- mavpd.micro: Mean annual vapor pressure deficit per site in (kPa)
microclim_2009_2023_warmestquarter_means.csv
same column values as for microclim_2009_2023_annual_means.csv, but for microclimate variable output for the Austral summer - warmest quarter months of December to February
shade.csv - Vegetation shade data collected in plots at each site. Shade is measured at 9 1x1m square quadrats per plot with 4 plots per site, 8 plots per location. Shade variables used in climate_modelling.R for parameterizing the microclimate model.
Variables:
- Site: location name abbreviation matches to ‘location2’ column in mr_ant_525.csv
- p.categorical: phosphorus category of site (HP = high phosphorus, LP = low phosphorus)
- site.pcat: location name plus site phosphorus category (HP = high phosphorus, LP = low phosphorus)
- site.plot: location name plus site phosphorus category plus plot ID (1-4)
- lat: Latitude of plot
- long: Longitude of plot
- quadrat: Quadrat number per plot (1-9)
- shade: % vegetation cover aggregated shrub, canopy layers with maximum set to 100%
Code/software
File: mr_ant_models.R
Description: Data wrangling and main models used in analysis. All packages are provided at the beginning of the code. Code run with R version 4.1.1 "Kick Things"
File: mr_ant_supp_analysis.R
Description: Data wrangling and supplementary analysis to support the main models. All packages are provided at the beginning of the code. Code run with R version 4.1.1 "Kick Things"
File: climate_modelling.R
Description: File in zipped folder "mr_ant_climate_modelling.zip": Annotated R script to run analyses for obtaining microclimate data layers and macroclimate data layers used in the main analysis. Code run with R version 4.1.1 "Kick Things"
Study sites and ant sampling
Ants were sampled from six locations along the east coast and inland of south-eastern Australia representing a precipitation (421 – 1283 mm Mean Annual Precipitation) and temperature (13 – 20˚C Mean Annual Temperature) gradient. Locations were each sampled over a one-week period between June 2022 and April 2023, with locations sampled during the warmer months. Within each location, two sites with contrasting soil phosphorus status were chosen within which four plots (10 x 10 m), separated by ~200 m, were established (total of eight plots per site) and soil cores taken to confirm soil phosphorus levels. We collected as many live ant species as possible from each site over three days of sampling. We trialled a minimum of 10 individuals per colony taken from 1-3 colonies per species (30 individuals per species) from each site for metabolic assays.
Metabolic assays
Ants were housed at 20˚C in plastic nest boxes and provided with water and honey soaked in cotton wool balls every two days. Time between field collection and metabolic trials across sites ranged between 3 and 14 days. As digestion can influence metabolic rate, ants were starved but provided with water for 48 hours prior to trials. Carbon dioxide production (VCO2) was used as a proxy for metabolic rate and was measured at a consistent temperature of 22˚C using 8 Sable Systems International (SSI) multiple animal versatile energetics (MAVEn (SSI, Las Vegas, Nevada, USA)) system each attached to a Li-Cor 7000 CO2/H2O infrared gas analyser (Li-Cor, Lincoln, Nebraska, USA). Activity readings of each individual ant were measured simultaneously using infrared light detectors.
Following experiments, ants were instantly frozen at -20 °C and wet mass was measured the following day. Ants were then dried at 50 °C for 48 hours and then weighed for dry mass. Data from assays was extracted using the software Expedata (SSI) and metabolic rate converted to microwatts per hour. Data were inspected and cleaned for technical errors and outliers resulting in a final dataset of 2805 individuals of 214 colonies. We found no relationship between activity during assays and metabolic rate and activity was therefore not included in downstream analyses.
Ventilation patterns
Insects are known to exhibit three forms of gas exchange: discontinuous gas exchange cycle (DGC), cyclic gas exchange and continuous gas exchange. We calculated three metrics to indicate DGC occurrence and frequency. We produced a binary (0,1) categorical value for whether DGC was being exhibited by a species. We then calculated the proportion of individuals per species for each site and plot that were conducting DGC, which ranged from 0 (no individuals exhibiting DGC) to 1 (all individuals exhibiting DGC). We then calculated ventilation frequency per hour (VF), for those individuals exhibiting a DGC pattern. Ventilation frequency was determined by first counting the number of complete closed and open phases in an individual’s VCO2 trace. Then multiplying this number by six (i.e., six 10-minute periods in an hour) to give vent frequency in cycles per hour.
Microclimate variables
We modelled microclimate at each site to represent the thermal and hydric environment which ant species directly experience in the field. We estimated hourly temperature and relative humidity for the 15 years preceding the study and calculated mean annual microclimate temperature and vapor pressure deficit (VPD), with VPD representing the aridity gradient using NicheMap R (Kearney et al. 2020).