Predicting growth and fitness responses of insects to climate change: Beyond thermal performance curves

2016 ◽  
Author(s):  
Joel Kingsolver
Keyword(s):  
Climate Change ◽  
Thermal Performance ◽  
Performance Curves

scholarly journals Use and misuse of temperature normalisation in meta-analyses of thermal responses of biological traits

2017 ◽  
Author(s):  
Dimitrios - Georgios Kontopoulos ◽  
Bernardo García-Carreras ◽  
Sofía Sal ◽  
Thomas P. Smith ◽  
Samraat Pawar
Keyword(s):  
Climate Change ◽  
Thermal Performance ◽  
Alternative Methods ◽  
Biological Traits ◽  
Thermal Physiology ◽  
Wide Range ◽  
Performance Curves ◽  
Biological Impacts ◽  
Using Data ◽  
Meta Analyses

There is currently unprecedented interest in quantifying variation in thermal physiology among organisms in order to understand and predict the biological impacts of climate change. A key parameter in this quantification of thermal physiology is the performance or value of a trait, across individuals or species, at a common temperature (temperature normalisation). An increasingly popular model for fitting thermal performance curves to data – the Sharpe-Schoolfield equation – can yield strongly inflated estimates of temperature-normalised trait values. These deviations occur whenever a key thermodynamic assumption of the model is violated, i.e. when the enzyme governing the performance of the trait is not fully functional at the chosen reference temperature. Using data on 1,758 thermal performance curves across a wide range of species, we identify the conditions that exacerbate this inflation. We then demonstrate that these biases can compromise tests to detect metabolic cold adaptation, which requires comparison of fitness or trait performance of different species or genotypes at some fixed low temperature. Finally, we suggest alternative methods for obtaining unbiased estimates of temperature-normalised trait values for meta-analyses of thermal performance across species in climate change impact studies.

scholarly journals Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures?

2016 ◽  
Vol 19 (11) ◽  
pp. 1372-1385 ◽  
Author(s):  
Brent J. Sinclair ◽  
Katie E. Marshall ◽  
Mary A. Sewell ◽  
Danielle L. Levesque ◽  
Christopher S. Willett ◽  
...  
Keyword(s):  
Climate Change ◽  
Thermal Performance ◽  
Body Temperatures ◽  
Performance Curves

scholarly journals Use and misuse of temperature normalisation in meta-analyses of thermal responses of biological traits

2017 ◽  
Author(s):  
Dimitrios - Georgios Kontopoulos ◽  
Bernardo García-Carreras ◽  
Sofía Sal ◽  
Thomas P. Smith ◽  
Samraat Pawar
Keyword(s):  
Climate Change ◽  
Thermal Performance ◽  
Alternative Methods ◽  
Biological Traits ◽  
Thermal Physiology ◽  
Wide Range ◽  
Performance Curves ◽  
Biological Impacts ◽  
Using Data ◽  
Meta Analyses

There is currently unprecedented interest in quantifying variation in thermal physiology among organisms in order to understand and predict the biological impacts of climate change. A key parameter in this quantification of thermal physiology is the performance or value of a trait, across individuals or species, at a common temperature (temperature normalisation). An increasingly popular model for fitting thermal performance curves to data – the Sharpe-Schoolfield equation – can yield strongly inflated estimates of temperature-normalised trait values. These deviations occur whenever a key thermodynamic assumption of the model is violated, i.e. when the enzyme governing the performance of the trait is not fully functional at the chosen reference temperature. Using data on 1,758 thermal performance curves across a wide range of species, we identify the conditions that exacerbate this inflation. We then demonstrate that these biases can compromise tests to detect metabolic cold adaptation, which requires comparison of fitness or trait performance of different species or genotypes at some fixed low temperature. Finally, we suggest alternative methods for obtaining unbiased estimates of temperature-normalised trait values for meta-analyses of thermal performance across species in climate change impact studies.

scholarly journals Use and misuse of temperature normalisation in meta-analyses of thermal responses of biological traits

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4363 ◽  
Author(s):  
Dimitrios - Georgios Kontopoulos ◽  
Bernardo García-Carreras ◽  
Sofía Sal ◽  
Thomas P. Smith ◽  
Samraat Pawar
Keyword(s):  
Climate Change ◽  
Thermal Performance ◽  
Alternative Methods ◽  
Biological Traits ◽  
Thermal Physiology ◽  
Wide Range ◽  
Performance Curves ◽  
Biological Impacts ◽  
Using Data ◽  
Meta Analyses

There is currently unprecedented interest in quantifying variation in thermal physiology among organisms, especially in order to understand and predict the biological impacts of climate change. A key parameter in this quantification of thermal physiology is the performance or value of a rate, across individuals or species, at a common temperature (temperature normalisation). An increasingly popular model for fitting thermal performance curves to data—the Sharpe-Schoolfield equation—can yield strongly inflated estimates of temperature-normalised rate values. These deviations occur whenever a key thermodynamic assumption of the model is violated, i.e., when the enzyme governing the performance of the rate is not fully functional at the chosen reference temperature. Using data on 1,758 thermal performance curves across a wide range of species, we identify the conditions that exacerbate this inflation. We then demonstrate that these biases can compromise tests to detect metabolic cold adaptation, which requires comparison of fitness or rate performance of different species or genotypes at some fixed low temperature. Finally, we suggest alternative methods for obtaining unbiased estimates of temperature-normalised rate values for meta-analyses of thermal performance across species in climate change impact studies.

scholarly journals rTPC and nls.multstart: a new pipeline to fit thermal performance curves in R

2020 ◽  
Author(s):  
Daniel Padfield ◽  
Hannah O’Sullivan ◽  
Samraat Pawar
Keyword(s):  
Climate Change ◽  
Model Selection ◽  
Thermal Performance ◽  
Model Fitting ◽  
Multiple Models ◽  
List Type ◽  
Linear Least Squares ◽  
Wide Range ◽  
Performance Curves ◽  
Weighted Model

AbstractThe quantification of thermal performance curves (TPCs) for biological rates has many applications to problems such as predicting species’ responses to climate change. There is currently no widely used open-source pipeline to fit mathematical TPC models to data, which limits the transparency and reproducibility of the curve fitting process underlying applications of TPCs.We present a new pipeline in R that currently allows for reproducible fitting of 24 different TPC models using non-linear least squares (NLLS) regression. The pipeline consists of two packages – rTPC and nls. multstart – that allow multiple start values for NLLS fitting and provides helper functions for setting start parameters. This pipeline overcomes previous problems that have made NLLS fitting and estimation of key parameters difficult or unreliable.We demonstrate how rTPC and nls.multstart can be combined with other packages in R to robustly and reproducibly fit multiple models to multiple TPC datasets at once. In addition, we show how model selection or averaging, weighted model fitting, and bootstrapping can easily be implemented within the pipeline.This new pipeline provides a flexible and reproducible approach that makes the challenging task of fitting multiple TPC models to data accessible to a wide range of users.

scholarly journals Phytoplankton thermal responses adapt in the absence of hard thermodynamic constraints

2018 ◽  
Author(s):  
Dimitrios - Georgios Kontopoulos ◽  
Erik van Sebille ◽  
Michael Lange ◽  
Gabriel Yvon-Durocher ◽  
Timothy G. Barraclough ◽  
...  
Keyword(s):  
Thermal Performance ◽  
Thermal Sensitivity ◽  
Meta Analysis ◽  
Habitat Type ◽  
Performance Curve ◽  
Thermal Environments ◽  
Thermal Performance Curve ◽  
Performance Curves ◽  
Minor Influence ◽  
A Minor

AbstractTo better predict how populations and communities respond to climatic temperature variation, it is necessary to understand how the shape of the response of fitness-related traits to temperature evolves (the thermal performance curve). Currently, there is disagreement about the extent to which the evolution of thermal performance curves is constrained. One school of thought has argued for the prevalence of thermodynamic constraints through enzyme kinetics, whereas another argues that adaptation can—at least partly—overcome such constraints. To shed further light on this debate, we perform a phylogenetic meta-analysis of the thermal performance curves of growth rate of phytoplankton—a globally important functional group—, controlling for environmental effects (habitat type and thermal regime). We find that thermodynamic constraints have a minor influence on the shape of the curve. In particular, we detect a very weak increase of maximum performance with the temperature at which the curve peaks, suggesting a weak “hotter-is-better” constraint. Also, instead of a constant thermal sensitivity of growth across species, as might be expected from strong constraints, we find that all aspects of the thermal performance curve evolve along the phylogeny. Our results suggest that phytoplankton thermal performance curves adapt to thermal environments largely in the absence of hard thermodynamic constraints.

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