DOI: 10.5061/dryad.d7wm37qb7
title: Metabolic traits are shaped by phylogenetic conservatism and environment, not just body size

description: 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.
description: <em>Study sites and ant sampling</em>
 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. <em>Metabolic assays</em>
 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 (VCO<sub>2</sub>) 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<sup> </sup>(SSI, Las Vegas, Nevada, USA)) system each
 attached to a Li-Cor 7000
 CO<sub>2</sub>/H<sub>2</sub>O 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. <em>Ventilation
 patterns</em> 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
 VCO<sub>2</sub> trace. Then multiplying this number by six
 (i.e., six 10-minute periods in an hour) to give vent frequency in cycles
 per hour. <em>Microclimate
 variables</em> 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).
description: # Metabolic traits are shaped by phylogenetic conservatism and
 environment, not just body size
 [https://doi.org/10.5061/dryad.d7wm37qb7](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"