Thermal Performance Curves and the Metabolic Theory of Ecology—A Practical Guide to Models and Experiments for Parasitologists
Bibliographic record
Abstract
Climate change will affect host-parasite dynamics in complex ways. The development of forecast models is necessary for proactive disease management, but past studies have frequently reported thermal performance data in idiosyncratic ways that have limited use for parameterizing thermal host-parasite models. Development of improved forecast models will require strong collaborations between experimental parasitologists and disease modelers. The purpose of this article is to facilitate such collaborations by reviewing practical considerations for describing thermal performance curves of parasite and host performance traits, and using them to predict climate change impacts on host-parasite systems. In the first section, we provide an overview of how thermal performance curves can be embedded in life-cycle-based dynamical models of parasitism, and we outline how such models can capture the net effect of multiple nonlinear temperature dependencies affecting the host-parasite dynamics. We also discuss how macroecological generalities based on the metabolic theory of ecology (MTE) can be used to determine a priori parameter estimates for thermal performance curves to derive null models for data-deficient species, but we note that most of the generalities suggested by MTE remain to be tested for parasites. In the second section, we discuss empirical knowledge gaps for the temperature dependence of parasite and host performance traits, and we outline the types of data that need to be collected to inform MTE-based models for data-deficient species. We specifically emphasize the importance of (1) capturing the entire thermal response of performance traits, including lower and upper temperature thresholds, and (2) experimentally or statistically separating out the thermal responses of different performance traits (e.g., development and mortality) rather than only reporting composite measures (e.g., apparent development). Not adhering to these principles can lead to biased climate change impact predictions. In the third section, we provide a practical guide outlining how experimentalists can contribute to fill data gaps by measuring the temperature dependence of host and parasite performance traits in ways that are systematic, statistically rigorous, and consistent with the requirements of life cycle-based host-parasite models. This guide includes recommendations and practical examples illustrating (1) the use of perturbation analyses to determine experimental priorities, (2) experimental design tips for quantifying thermal response curves, and (3) statistical methods for estimating the parameters of thermal performance curves. Our hope is that this article helps researchers to maximize the value and use of future data collections for both empirical and modelling studies investigating the way in which temperature influences parasitism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".