Data and code from: Predicting the fundamental thermal niche of ectotherms
Data files
Mar 04, 2024 version files 242.09 KB

BagradaAmort

BagradaFec

BagradaJmort

BagradaMatRate

HarlequinAmort

HarlequinFec

HarlequinJmort

HarlequinMatRate

README.md

rFromMathematicaB2023.csv

rFromMathematicaH2023.csv
Abstract
Climate warming is predicted to increase mean temperatures and thermal extremes on a global scale. Because their body temperature depends on the environmental temperature, ectotherms bear the full brunt of climate warming. Predicting the impact of climate warming on ectotherm diversity and distributions requires a framework that can translate temperature effects on ectotherm life history traits into population and communitylevel outcomes. Here we present a mechanistic theoretical framework that can predict the fundamental thermal niche and climate envelope of ectotherm species based on how temperature affects the underlying life history traits. The advantage of this framework is twofold. First, it can translate temperature effects on the phenotypic traits of individual organisms to populationlevel patterns observed in nature. Second, it can predict thermal niches and climate envelopes based solely on trait response data and hence completely independently of any populationlevel information. We find that the temperature at which the intrinsic growth rate is maximized exceeds the temperature at which abundance is maximized under densitydependent growth. As a result, the temperature at which a species will increase the fastest when rare is lower than the temperature at which it will recover from a perturbation the fastest when abundant. We test model predictions using data from a nativeinvasive interaction to identify the temperatures at which the invader can most easily invade the native's habitat, and the native species is most likely to resist the invader. The framework is sufficiently mechanistic to yield reliable predictions for individual species, and sufficiently broad to apply across a range of ectothermic taxa. This ability to predict the thermal niche before a species encounters a new thermal environment is essential to mitigating some of the major effects of climate change on ectotherm populations around the globe.
README
## Description of the data and file structure
This repository includes the following files:
Bagrada and harlequin data files containing the life history trait response data of the bagrada and harlequin bugs. These include the following files in csv format:
1) BagradaAmort: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average adult deaths per day), 95.CI.min (lower 95% confidence interval), 95.CI.max (upper 95% confidence interval), SE (standard error)
2) BagradaFec: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average eggs laid per female per day), 95.CI.min (lower 95% confidence interval), 95.CI.max (upper 95% confidence interval), SE (standard error)
3) BagradaJmort: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average juvenile deaths per day), 95.CI.min (lower 95% confidence interval), 95.CI.max (upper 95% confidence interval), SE (standard error)
4) BagradaMatRate: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average of inverse of maturation time per individual juvenile; maturation time is the time it takes an individual to go from newly hatched to its emergence from its 5th instar (final) molt), 95.CI.min (lower 95% confidence interval), 95.CI.max (upper 95% confidence interval), SE (standard error)
5) HarlequinAmort: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average adult deaths per day), SE (standard error)
6) HarlequinFec: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average eggs laid per female per day), SE (standard error)
7) HarlequinJmort: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average juvenile deaths per day), SE (standard error)
8) HarlequinMatRate: columns are Temp.K (temperature in degrees Kelvin), Average.rate.per.day (average of inverse of maturation time per individual juvenile; maturation time is the time it takes an individual to go from newly hatched to its emergence from its 5th instar (final) molt), SE (standard error)
9) rFromMathematicaB2023.csv: bagrada intrinsic growth rate output generated from
10) rFromMathematicaH2023.csv: harlequin intrinsic growth rate output generated from
These csv files can be opened and viewed in any text editor and can be read into R using 'read.csv()'.
## Code/Software
The following files used to analyze the data and generate figures are available on Zenodo.
1) IntrinsicGrowthRateCalculation_MathematicaCode.nb: Mathematica code that outputs highly accurate intrinsic growth rate of harlequin and bagrada (see rFromMathematicaB2023.csv and rFromMathematicaH2023.csv). These growth rates were used in generation of Fig. 4. Mathematica was used for this because niche overlap was calculated in Mathematica, and there were very tiny differences between R and Mathematica predictions for bagrada and harlequin metrics likely stemming from differences in how the Lambert W function (referred to as a ProductLog in Mathematica) was calculated. We chose to plot Fig. 4 based on Mathematica's predictions because defining T_intersect, determined in Mathematica, is a key part of plotting this figure and niche overlap, presented on the figure, can only be calculated in Mathematica (see Fig. 4 section of
2) NicheOverlap_MathematicaCode.nb: Mathematica file used to solve niche overlap Eq. (14) in the main text
3) NicheOverlap_MathematicaCode_OUTPUT.pdf: pdf version of
4) ParameterEstimation_Rcode.Rmd: R markdown file showing trait response parameterization from bagrada and harlequin data using nls()
5) ParameterEstimation_Rcode_OUTPUT.pdf: pdf of
6) Simulation_FigureGeneration_Rcode.R: R file in which all DI simulation occurs, bagrada and harelquin metrics for Table 2 are calculated, and all main text and Appendix figures are generated
Methods
Please see Sections 4.2.2 and 4.2.3 of the Simon and Amarasekare Ecology paper for details on the experiments and parameter estimation approach used to collect and process this dataset.