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Limited plasticity in thermally tolerant ectotherm populations: evidence for a trade-off

Citation

Barley, Jordanna et al. (2021), Limited plasticity in thermally tolerant ectotherm populations: evidence for a trade-off, Dryad, Dataset, https://doi.org/10.5061/dryad.zs7h44j8z

Abstract

Many species face extinction risks owing to climate change, and there is an urgent need to identify which species' populations will be most vulnerable. Plasticity in heat tolerance, which includes acclimation or hardening, occurs when prior exposure to a warmer temperature changes an organism's upper thermal limit. The capacity for thermal acclimation could provide protection against warming, but prior work has found few generalizable patterns to explain variation in this trait. Here, we report the results of, to our knowledge, the first meta-analysis to examine within-species variation in thermal plasticity, using results from 20 studies (19 species) that quantified thermal acclimation capacities across 78 populations. We used meta-regression to evaluate two leading hypotheses. The climate variability hypothesis predicts that populations from more thermally variable habitats will have greater plasticity, while the trade-off hypothesis predicts that populations with the lowest heat tolerance will have the greatest plasticity. Our analysis indicates strong support for the trade-off hypothesis because populations with greater thermal tolerance had reduced plasticity. These results advance our understanding of variation in populations' susceptibility to climate change and imply that populations with the highest thermal tolerance may have limited phenotypic plasticity to adjust to ongoing climate warming.

Methods

Studies which measure thermal tolerance plasticity typically collect organisms from nature and acclimate them to different temperatures for defined periods of time in the lab before measuring thermal tolerance. Important methodological considerations for these types of studies include (1) for how long and at what temperature organisms are acclimated, (2) how fast the temperature is changed, and (3) whether measurements are made on field collected or F1 individuals, given that parental conditions are known to influence thermal plasticity. To identify publications for use in our study, we followed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines (supplemental figure S1). We searched titles, abstracts, and key words in Web of Science (Clarivate Analytics, Philadelphia, USA) using the following search string: (Thermal OR temperatures) AND (Lethal OR “Thermal tolerance” OR “Thermal limit” OR CTmax OR CTmin OR LT50 OR “freezing tolerance”) AND ("Local* Adapt*" OR "“Latitud* Var” OR Intraspecific). We conducted an initial literature search on 24 August 2019 and updated the search on 28 July 2020. We also added studies that we were aware of but were not returned in the literature search. We used the following criteria for inclusion, where each study must have: (1) reported new results of whole-organism upper thermal limit in degrees Celsius for at least 2 populations of the same species, (2) experimentally measured thermal tolerance after acclimating all individuals to at least two temperatures, (3) reported a measure of error for the thermal tolerance estimate, and (4) not measured tolerance of introduced species, hybrid lines, cultivars, domesticated species, or later generations of experimental laboratory populations (greater than F2). Although we initially included both cold and heat tolerance studies, we later excluded cold tolerance studies from our analysis due to insufficient data. Studies of cold tolerance, and additional studies that met criteria (1) and (2) but were excluded based on (3) and/or (4) are listed in supplemental table S1. We screened 400 publications that measured thermal tolerances across populations and identified 20 studies to include in our meta-analysis, representing 19 species. The studies that were accepted after looking through titles and abstracts but were later excluded from our analysis are listed in Table S1. We extracted data from each publication using relevant figures, tables, text, or supplementary material. In the case of figures, we used WebPlotDigitizer to extract means and error estimates.

Usage Notes

GENERAL INFORMATION

1. Title of Dataset: acc_data_commoncontrol.csv and acc_data_reformatted_sorted.xlsx

2. Author Information
    A. Principal Investigator Contact Information
        Name: Jordanna Barley
        Institution: University of Massachusetts Amherst
        Address: 160 Holdsworth Way, Amherst, MA 01003
        Email: jbarley@umass.edu

3. Date of data collection: 2019-08-24 and 2020-07-28

4. Geographic location of data collection: CT, USA


DATA & FILE OVERVIEW

1. File List:
acc_data_commoncontrol.csv: dataset used in the main weighted meta-analytic models
acc_data_reformatted_sorted.xslx: dataset used earlier in the analysis process before the common control approach was decided on


DATA-SPECIFIC INFORMATION FOR: acc_data_commoncontrol.csv and acc_data_reformatted_sorted.xlsx

1. Number of variables: 33

2. Variable List:
study: study data was extracted from
taxon: latin name of species
phylum: phylum species bolongs to
common_name: common name of species
number_of_populations: number of populations for each species
ecosystem: ecosystem species inhabits (ocean, intertidal, terrestrial, freshwater)  
dispersal_mode: mode of dispersal for larval phase
acclimation_time: time researchers acclimated individuals in GENERATION TIME (ie. less than one generation, one generation, etc)
source_population: population that individual was obtained from, as defined by the researcher
latitude: latitude of the source population
longitude: longitude of the source population
life_history_stage: life history stage of the individual used in experimentation
sex: sex of the individual used in experimentation
acclimation_temperature_1: lower temperature in pairwaise contrast between acclimation temperature used
acclimation_temperature_2: upper temperature in pairwaise contrast between acclimation temperature used
measurement_level: level of the thermal tolerance measurement (ie. CTmax is an individual level measurement, LT50 is a population level measurement)
thermal_limit_type: type of thermal limit measuremed (CTmax, CTmin, LD50_high, LD50_lower)
thermal_limit_1: thermal limit for acclimation temperature #1
thermal_limit_2: thermal limit for acclimation temperature #2
thermal_limit_error_type: type of error association with the mean thermal limit (standard error, standard deviation, confidence intervals)
thermal_limit_error_1: error measurement for thermal_limit_1, given by the researchers
thermal_limit_error_2: error measurement for thermal_limit_2, given by the researchers
n_1: sample size for a given population 1
n_2: sample size for a given population 2
ni: total sample sized used for each population
max_temp: maximum temp. measured at the source population lat/long
min_temp: minimum temp. measured at the source population lat/long
mean_temp: mean temp. measured at the source population lat/long
temp_range: difference between max_temp and min_temp
upper_lower: factor distinguishing between upper and lower thermal limits
ARR: acclimation response ratio for a given comparison between two acclimation temperatures; (thermal limit 2- thermal limit 1)/(temp 2-temp 1)

Funding

National Science Foundation, Award: OCE-1764316

National Science Foundation, Award: OCE-1764316

National Science Foundation, Award: OCE-2023571

Shoals Marine Laboratory

Department of Environmental Conservation at the University of Massachusetts Amherst, Award: MAS00558

National Institute of Food and Agriculture

NIFA