The 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.

Understanding how changes in temperature affect interspecific competition is critical for predicting changes in ecological communities with global warming. Here, we develop a theoretical model that links interspecific differences in the temperature dependence of resource acquisition and growth to the outcome of pairwise competition in phytoplankton. We parameterised our model with these metabolic traits derived from six species of freshwater phytoplankton and tested its ability to predict the outcome of competition in all pairwise combinations of the species in a factorial experiment, manipulating temperature and nutrient availability. The model correctly predicted the outcome of competition in 72% of the pairwise experiments, with competitive advantage determined by difference in thermal sensitivity of growth rates of the two species. These results demonstrate that metabolic traits play a key role in determining how changes in temperature influence interspecific competition and lay the foundation for mechanistically predicting the effects of warming in complex, multi-species communities.

The degree to which arthropod populations will be able to adapt to climatic warming is uncertain. Here, we report that arthropod thermal adaptation is likely to be constrained in two fundamental ways. First, maximization of population fitness with warming is predicted to be determined predominantly by the temperature of peak performance of juvenile development rate, followed by that of adult fecundity, juvenile mortality and adult mortality rates, in this specific order. Second, the differences among the temperature of peak performance of these four traits will constrain adaptation. By compiling a new global dataset of 61 diverse arthropod species, we show that contemporary populations have indeed evolved under these constraints. Our results provide a basis for using relatively feasible trait measurements to predict the adaptive capacity of arthropod populations to climatic warming.

Laboratory-derived temperature-dependencies of life history traits are increasingly being used to make mechanistic predictions for how climatic warming will affect the abundance of disease vectors. These laboratory data are typically from populations reared on optimal resource supply, even though natural populations are expected to experience fluctuations in resource availability.Using laboratory experiments and stage-structured population projection modelling, here we ask how resource limitation affects temperature-dependence of life history traits and emergent fitness of a principal arbovirus vector, Aedes aegypti, across a temperature range it typically experiences (22–32°C).We show that low-resource supply significantly depresses the vector’s maximal population growth rate (rmax) across the entire temperature range and causes it to peak at a lower temperature, than under high-resource supply. This difference is driven by the fact that resource limitation significantly increases juvenile mortality, slows development, and reduces lifespan and size at maturity (which then decreases fecundity in adults). These results show that resource supply can significantly affect the temperature-dependence of population-level fitness of disease vectors by modifying the thermal responses of underlying traits.Our study suggests that by ignoring resource limitation, projections of vector abundance and disease transmission based on laboratory studies are likely to substantially underestimate the effect of temperature on development time and juvenile survival, and overestimate the effect of temperature on lifespan, size and fecundity.Our results provide compelling evidence for future studies to consider resource supply when making predictions about the effects of climate and habitat change on disease vectors. More generally, our results point at the need to consider the effects of resource limitation on temperature-dependence of life history traits to further advance Ecological Metabolic Theory and improve its utility for predicting the responses of holometabolous insects to climate change.

11Understanding how the metabolic rates of prokaryotes respond to temperature is fun-12 damental to our understanding of how ecosystem functioning will be altered by climate 13 change, as these micro-organisms are major contributors to global carbon efflux. Ecological 14 metabolic theory suggests that species living at higher temperatures evolve higher growth 15 rates than those in cooler niches due to thermodynamic constraints. Here, using a global 16 prokaryotic dataset, we find that maximal growth rate at thermal optimum increases with 17 temperature for mesophiles (temperature optima 45 • C), but not thermophiles ( 45 • C). 18 Furthermore, short-term (within-day) thermal responses of prokaryotic metabolic rates are 19 typically more sensitive to warming than those of eukaryotes. Given that climatic warming 20 will mostly impact ecosystems in the mesophilic temperature range, we conclude that as 21 microbial communities adapt to higher temperatures, their metabolic rates and therefore, 22 carbon efflux, will inevitably rise. Using a mathematical model, we illustrate the potential 23 global impacts of these findings. 24 Introduction 25 A general understanding of how individual organisms respond to changing environmental temperature 26 is necessary for predicting how populations, communities and ecosystems will respond to a changing 27 climate 1,2,3,4 . Because fundamental physiological rates of ectotherms are directly affected by environ-28 1 mental temperature 3,5,6 , climatic warming may be expected to lead to ectotherm communities with 29 higher metabolic rates on average 3,7 . How environmental temperature drives metabolic rates of prokary-30 otes (bacteria and archaea) is of particular importance because they are globally ubiquitous, estimated 31 to comprise up to half of the planet's global biomass 8 , and consume (respire) the majority of net primary 32 production 9,10 . Therefore, climate-driven changes in prokaryotic metabolic rates are expected to signif-33 icantly alter ecosystem productivity, nutrient cycling, and carbon flux 9,10,11,12,13,14 . Indeed, increased 34 carbon efflux has been observed in experimental measures of soil CO 2 loss to warming 15,16 , as well as 35 the responses of other microbial metabolic processes to increased temperature such as methanogenesis 17 . 36However, whether the short-term (timescales of minutes to days) thermal responses of prokaryotes can be 37 compensated by acclimation (physiological phenotypic plasticity) or longer-term (timescales of years or 38 months, years or longer) evolutionary adaptation 18,19,20 is currently unclear. The most recent study to 39 investigate this idea concluded that both short-and long-term responses of ecosystem-level heterotrophic 40 respiration were similar 21 . However, this study quantified short-term responses by aggregating day-level 41 carbon fluxes across sites, and did not have data on the direct respiratory contributions of prokaryotyes 42 per se. 43The short term, or "instantaneous" response of metab...