Metabolic approaches to understanding climate change impacts on seasonal host‐macroparasite dynamics
Abstract Climate change is expected to alter the dynamics of infectious diseases around the globe. Predictive models remain elusive due to the complexity of host–parasite systems and insufficient data describing how environmental conditions affect various system components. Here, we link host–macrop...
| Published in: | Ecology Letters |
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| Main Authors: | , , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2012
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1111/ele.12022 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fele.12022 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.12022 |
| Summary: | Abstract Climate change is expected to alter the dynamics of infectious diseases around the globe. Predictive models remain elusive due to the complexity of host–parasite systems and insufficient data describing how environmental conditions affect various system components. Here, we link host–macroparasite models with the Metabolic Theory of Ecology, providing a mechanistic framework that allows integrating multiple nonlinear environmental effects to estimate parasite fitness under novel conditions. The models allow determining the fundamental thermal niche of a parasite, and thus, whether climate change leads to range contraction or may permit a range expansion. Applying the models to seasonal environments, and using an arctic nematode with an endotherm host for illustration, we show that climate warming can split a continuous spring‐to‐fall transmission season into two separate transmission seasons with altered timings. Although the models are strategic and most suitable to evaluate broad‐scale patterns of climate change impacts, close correspondence between model predictions and empirical data indicates model applicability also at the species level. As the application of Metabolic Theory considerably aids the a priori estimation of model parameters, even in data‐sparse systems, we suggest that the presented approach could provide a framework for understanding and predicting climatic impacts for many host–parasite systems worldwide. |
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