DOI: 10.5061/dryad.pg4f4qrxb
title: Data for: Evolution of avian heat tolerance: The role of atmospheric humidity

description: The role of atmospheric humidity in the evolution of endotherms’
 thermoregulatory performance remains largely unexplored, despite elevated
 atmospheric humidity being known to impede evaporative cooling capacity.
 Using a phylogenetically informed comparative framework, we tested the
 hypothesis that pronounced hyperthermia tolerance among birds occupying
 humid lowlands evolved to reduce the impact of humidity-impeded scope for
 evaporative heat dissipation by comparing heat tolerance limits (HTL;
 maximum tolerable air temperature), maximum body temperatures (Tbmax) and
 associated thermoregulatory variables in humid (19.2 g H2O m− 3) versus
 dry (1.1 g H2O m− 3) air among 30 species from three climatically distinct
 sites (arid, mesic montane and humid lowland). Humidity-associated
 decreases in evaporative water loss and resting metabolic rate were 27 -
 38% and 21 - 27%, respectively, and did not differ significantly between
 climatic sites. Decreases in heat tolerance limits were significantly
 larger among arid-zone (mean ± SD = 3.13 ± 1.12 °C) and montane species
 (2.44 ± 1.0 °C) compared to lowland species (1.23 ± 1.34 °C), with more
 pronounced hyperthermia among lowland (Tbmax = 46.26 ± 0.48°C) and montane
 birds (Tbmax = 46.19 ± 0.92°C) compared to arid-zone species (45.23 ±
 0.24°C). Our findings reveal a functional link between facultative
 hyperthermia and humidity-related constraints on evaporative cooling,
 providing novel insights into how hygric and thermal environments interact
 to constrain avian performance during hot weather. Moreover, the
 macrophysiological patterns we report provide further support for the
 concept of a continuum from thermal specialization to thermal
 generalization among endotherms, with adaptive variation in body
 temperature correlated with prevailing climatic conditions.
description: Materials and Methods Study
 areas We collected data at three climatically distinct
 areas (hot arid, mesic montane and humid lowland) between latitudes of
 27.90° and 30.04° S in South Africa [see Table 1 for general information
 and climatic data (Smit et al. 2011, Fick and Hijmans 2017)].
 Study species We collected physiological
 data under either dry (~1 g H<sub>2</sub>O
 m<sup>-3</sup>) or humid conditions (~19 g
 H<sub>2</sub>O m<sup>-3</sup>). Overall, our
 analysis includes data from 627 individuals (humid, n = 307; dry, n = 320)
 representing 30 species, 15 families and three orders – Passeriformes,
 Piciformes and Coraciiformes. Humidity and dry protocol data were
 collected during the austral summer between January – February (2021)
 [arid-zone species (see Appendix S1: Table S3, S4 and S5a and S5b)] and
 September 2021 – February (2022) [montane species (see Appendix S1: Table
 S9 and S10) and lowland species (see Appendix S1: Table S6, S7 and S8)].
 Where available, data for species responses under dry air conditions at
 our montane and lowland sites were obtained from Freeman <em>et
 al.</em> (2022) (see Appendix S1: Table S1 and S2).
 Air and body temperature measurements A
 temperature-sensitive passive integrated transponder (PIT) tag (Biotherm
 13, Biomark, Boise, ID, USA) was injected into the peritoneal cavity of
 each bird prior to the commencement of experimentation to measure
 <em>T</em><sub>b</sub>. Data from the PIT tags
 were acquired using a reader and transceiver system (HPR +, Biomark, Boise
 ID, USA). During experimentation,
 <em>T</em><sub>air</sub> within the metabolic
 chamber was measured using a thermistor probe (TC-100, Sable Systems, Las
 Vegas, NV, USA) inserted through a small hole in the side of the chamber
 and sealed by a rubber grommet. Experimental
 protocol We measured
 <em>T</em><sub>b</sub>, evaporative water loss and
 resting metabolic rate using both the dry and humid protocols.
 Measurements typically lasted 2 – 4 h and began with a bird placed in a
 chamber at <em>T</em><sub>air</sub> = 28 °C and
 given at least 1 h to habituate. For the dry protocol,
 <em>T</em><sub>air</sub> setpoints beginning from
 <em>T</em><sub>air</sub> = 28 °C were initially
 increased incrementally by 4 °C to
 <em>T</em><sub>air</sub> = 40 °C and thereafter
 increased incrementally by 2 °C until birds reached their thermal
 endpoints, following Freeman <em>et al.</em> (2022).  For the
 humid protocol, following habituation at 28 °C<em>,
 T</em><sub>air</sub> was gradually increased to 34 °C,
 above which setpoints were increased incrementally by 2 °C until birds
 reached their thermal endpoints. Although the initial
 <em>T</em><sub>air </sub>setpoints differed
 between dry (28 °C) and humid (34 °C) protocols, the initial habituation
 periods at <em>T</em><sub>air</sub> = 28 °C prior
 to the commencement of data collection were identical and rates of heating
 similar between protocols. For these reasons, we do not think these
 differences between protocols had any effect on observed patterns of
 thermoregulation, particularly at
 <em>T</em><sub>air</sub> approaching the upper
 limits of thermoregulation. Transitions between successive
 <em>T</em><sub>air</sub> setpoints took 10–15 min.
 At each setpoint <em>T</em><sub>air</sub>, birds
 were exposed to stable <em>T</em><sub>air</sub>
 and humidity for a minimum of 15-20 minutes until concentrations of
 CO<sub>2</sub> and H<sub>2</sub>O were stable for
 at least 5 min. We used the stepped respirometry protocol involving brief
 (15-20 min) exposure to each
 <em>T</em><sub>air</sub> setpoint which has been
 shown to yield patterns of evaporative water loss, metabolic rate and
 <em>T<sub>b</sub></em> similar to those using a
 steady-state protocol where birds experience each
 <em>T</em><sub>air</sub> setpoint for several
 hours (Short et al. 2022). Gas exchange
 measurements Evaporative water loss and carbon dioxide
 production ( ) were measured using an open flow-through respirometry
 system, with our set-up identical to that described by Freeman
 <em>et al.</em> (2020, 2022) and described in full in Appendix
 S1: Section S1 of the supplementary material. During
 dry air measurements, flow rates were adjusted to minimise water vapour
 pressure within the metabolic chamber (mean chamber humidity across sites
 = 1.07 ± 0.84 g H<sub>2</sub>O m<sup>−
 </sup><sup>3</sup>) and varied between 3 L
 min<sup>− 1</sup> and 24 L min<sup>− 1</sup>.
 Humidity measurements were made following methods similar to the dry
 protocol, with modifications to the respirometry setup permitting the
 manipulation of in-chamber humidity (see Appendix S1: Figure S1
 and<em> </em>Appendix S1: Section S1 for detailed
 description). During our humidity measurements, mean chamber absolute
 humidity was 19.21 ± 1.20 g H<sub>2</sub>O m<sup>−
 3</sup> and varied by &lt; 1.5 g H<sub>2</sub>O
 m<sup>− 3</sup> between sites. Data
 analyses Sample sizes (n) for dry or humidity
 treatments were generally n = 10 individuals per species, but lower for a
 few (Appendix S1: Table S1). All data were analysed in the R 4.0.5 (R Core
 Team, 2020) environment, using R Studio 1.1.463 (RStudio, Inc.). For each
 species, respective inflection
 <em>T</em><sub>air</sub> values above which
 <em>T</em><sub>b</sub>, evaporative water loss,
 the ratio of evaporative heat loss to metabolic heat production (EHL/MHP)
 and metabolic rate increased rapidly were identified using the R package
 segmented.lme (Muggeo 2016), with individual identity included as a random
 effect to account for measurements at multiple
 <em>T</em><sub>air</sub> values per individual to
 avoid pseudoreplication. Each response variable including
 T<em><sub>b</sub></em>, evaporative water loss,
 and metabolic rate were analysed above and below inflection points
 separately using linear mixed-effect models in the R package nlme
 (Pinheiro J, Bates D, DebRoy S, Sarkar D 2015). Slopes for the
 relationships of thermoregulatory response variables were estimated as
 functions of <em>T</em><sub>air</sub>.
 We used the “dredge” function in the <em>MuMIn</em>
 package to undertake model selection (Barton 2019). The standardised model
 used for within-species analysis included
 <em>T</em><sub>air</sub> (or
 <em>T</em><sub>air</sub><em>−T</em><sub>b</sub>), <em>M</em><sub>b</sub>, the <em>T</em><sub>air</sub>: <em>M</em><sub>b</sub> interaction and Bird ID (individual) as a random factor. The model with the highest rank among competing models was selected using the AIC<sub>c</sub>(Akaike information criterion values corrected for small sample size) as well as the Akaike weights (Burnham and Anderson 2002). If competing models were within ΔAIC<sub>c</sub>&lt; 2, we selected the most parsimonious model. Significance was assessed at α &lt; 0.05 and values are presented as mean ± SD. Interspecific analyses We accounted for the effect of phylogenetic non-independence in observed patterns of our thermoregulatory response variables by constructing a maximum-likelihood tree including all study species using Mesquite (Maddison and Maddison 2014). Making use of the Hackett phylogeny as a back-bone (Hackett et al. 2008), we downloaded 100 phylogenies from www.birdtree.org (Jetz et al. 2012). Determining the necessary branch-length transformations was achieved by comparing an Ornstein-Uhlenbeck model (Martins and Hansen 1997) with a Brownian motion model of trait evolution (Grafen 1989) using AIC values. Lower AIC values were attained for the Brownian motion model and it was therefore retained. We used Pagel’s λ (Pagel 1999) to test for phylogenetic signal in the residual error of our PGLS (phylogenetic least square regression models) while simultaneously estimating regression parameters (Revell 2010),and rescaled our models using the estimates of λ. When testing for λ we included mean M<sub>b</sub> for species to account for the known allometric scaling of physiological traits such as basal metabolic rate (McNab 2002, McKechnie and Wolf 2004) and heat tolerance limit (van Jaarsveld et al. 2021). We detected a significant phylogenetic signal for heat tolerance limit (λ = 0.50), metabolic rate (λ = 0.95), evaporative water loss (λ = 0.85) and EHL_MHP (λ = 0.47) across climatic study sites, and we therefore present results from our PGLS analysis and phylogenetically informed post hoc tests (PhylANOVA). The results of conventional analysis (i.e., phylogenetic non-independence not controlled for) are available in the supplementary material (Appendix S1: Table S11). Conventional analyses are mostly consistent with our findings following phylogenetic correction. The R package <em>caper</em> (Orme et al. 2012) along with the “pgls” function was used to conduct phylogenetic regression analyses. To detect patterns and quantify differences in heat tolerance limit between climatic study areas as well as determine which physiological variables were predictors of heat tolerance limit patterns, we constructed a phylogenetically informed linear mixed effects model (Appendix S1: Table S12). The <em>MuMIn</em> package and “dredge” function was again used to detect which model selection procedure best explained observed patterns of heat tolerance limit under humid conditions (Barton 2019). Model selection was conducted using AICc values and weights. In addition to model selection, we ran analyses to detect auto-correlation among predictor variables (Appendix S1: Table S13, Durbin Watson test) and assessed the normality of residuals using a Shapiro-Wilk test. Model 328 (Heat tolerance limit ∼ Climate + MaxEHL/MHP + EvapScope + Tbmax + MaxEHL/MHP:climate; Appendix S1: Table S12) was selected. The <em>anova.pgls</em> function in the R-package <em>caper </em>(Orme et al. 2012) was applied to our model output to determine the significance of predictor variables and assess whether the response variable differed significantly among study localities (Appendix S1: Table S11). We conducted post hoc multiple comparison tests taking into account phylogenetic relatedness using the PhylANOVA function in the R package <em>phytools</em> (Revell 2012) to obtain pairwise differences in both response and predictor variables between climatic areas. The PhylANOVA function conducts a simulation-based phylogenetic ANOVA and performs all post hoc comparisons of means among groups providing a p-value by phylogenetic simulation (Garland et al. 1993).
description: This README file was generated on 2024-01-27 by Marc Freeman GENERAL
 INFORMATION 1. Title of Dataset: Data for: Evolution of avian heat
 tolerance: The role of atmospheric humidity 2. Author Information
 Corresponding Investigator Name: Dr Marc Trevor Freeman Institution: South
 African National Biodiversity Institute and University of Pretoria,
 Pretoria, South Africa Email: 
 Co-investigator 1 Name: Ms Bianca Coulson Institution: South African
 National Biodiversity Institute and University of Pretoria, Pretoria,
 South Africa Co-investigator 2 Name: Mr James Curtis Short Institution:
 South African National Biodiversity Institute and University of Pretoria,
 Pretoria, South Africa Co-investigator 3 Name: Dr Celiwe Angel
 Ngcamphalala Institution: Department of Biological Sciences, University of
 Cape Town, Cape Town, South Africa Co-investigator 4 Name: Mr Mathome Otto
 Makola Institution: South African National Biodiversity Institute and
 University of Pretoria, Pretoria, South Africa Co-investigator 5 Name:
 Prof. Andrew Edward McKechnie Institution: South African National
 Biodiversity Institute and University of Pretoria, Pretoria, South Africa
 3. Date of data collection: 2021-2022 4. Geographic location of data
 collection: Kamiesberg mountains, Harrismith and Hluhluwe, South Africa 5.
 Funding sources that supported the collection of the data: National
 Research Foundation (grant 119754) 6. Recommended citation for this
 dataset: Freeman, Marc; Coulson, Bianca; Short, James; Ngcamphalala,
 Celiwe; Makola, Mathome; McKechnie, Andrew (2024), Data for: Evolution of
 avian heat tolerance: The role of atmospheric humidity,
 DATA & FILE OVERVIEW
 1. Description of dataset Body temperatures, resting metabolic rates and
 rates of evaporative water loss in southern African birds, measured using
 flow-through respirometry at high air temperatures coupled with raised
 humidity. These data were collected to test the hypothesis that pronounced
 hyperthermia tolerance among birds occupying humid lowlands evolved to
 reduce the impact of humidity-impeded scope for evaporative heat
 dissipation. 1. File List: File 1 Name: Ecology Freeman et al 2024
 Data.xlsx File 1 Description: Body temperature, resting metabolic rate and
 evaporative water loss under dry (~1g H2O m-3) and humid conditions (~19g
 H2O m-3) (one species per tab). Also included are two additional sheets
 showing heat tolerance limit and maximum body temperature for arid,
 montane and lowland study species, respectively. METHODOLOGICAL
 INFORMATION Patterns of body temperature, evaporative water loss and
 resting metabolic rate (estimated from CO2 production) at air temperatures
 approaching or exceeding normothermic body temperature were quantified at
 three field sites in South Africa using flow-through respirometry. To
 elicit heat tolerance limits and maximum evaporative cooling capacity,
 birds experienced a stepped protocol of air temperature coupled with
 standardised humidity conditions (19g H2O m-3) increasing in 2 degree
 C-increments until thermal endpoints were reached, whereafter birds were
 removed from the metabolic chambers and cooled down. DATA-SPECIFIC
 INFORMATION FOR: Ecology Freeman et al 2024 Data.xlsx Species tabs: 1.
 Number of variables: 6 2. Number of cases/rows: varies among species 3.
 Variable List: Bird ID: individual identity Mass: body mass (g) Ta: air
 temperature (degree C) Tb: body temperature: (degree C) EWL: evaporative
 water loss (g / h) RMR: resting metabolic rate (W) 4. Missing data codes:
 None HTL and Tbmax tabs: 1. Number of variables: 5 2. Number of
 cases/rows: varies 3. Variable List: Nr: row number Bird ID: individual
 identity Species: species Tbmax: maximum body temperature reached (degree
 C) HTL: heat tolerance limit(degree C; maximum air temperature reached)