1. Modelling the potential distribution, net primary production and phenology of common ragweed with a physiological model.
- Author
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Leiblein‐Wild, Marion Carmen, Steinkamp, Jörg, Hickler, Thomas, and Tackenberg, Oliver
- Subjects
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AMBROSIA artemisiifolia , *PRIMARY productivity (Biology) , *PLANT phenology , *PHYSIOLOGICAL models , *PLANTS ,POTENTIAL distribution - Abstract
Aim Common ragweed ( Ambrosia artemisiifolia L.) is a medically relevant invasive species of great public interest due to its highly allergenic pollen. We aimed at modelling its potential range, its net primary production (NPP) and important phenological stages. Location Europe and North America. Methods We developed a new physiological model for common ragweed and applied it to simulate the species′ potential distribution (calibrated with the native range), NPP and phenology in North America and Europe. Based on this model, we investigated which regions are suitable for ragweed growth in Europe and simulated the timing of phenological stages that determine pollen release. Results The model predicted the observed distribution of ragweed in North America well. The application to Europe suggests that large parts of Europe are climatically suitable for ragweed growth and reproduction. The highest potential NPP was predicted in southern-central and south-eastern Europe and southern France, roughly corresponding with hotspots of atmospheric pollen load, but also indicating a higher potential than currently achieved in western Europe and along parts of the northern edge of its distribution. The predicted time of pollen releases in Europe corresponded well with measurements from pollen traps. Main conclusions The results suggest that our mechanistic model adequately represents physiological and ecological characteristics that determine the potential distribution, productivity and phenology of common ragweed. The model could be used for predicting the potential distribution and performance of ragweed in the future under climate change and might thus contribute to improved longer term predictions of exposure to allergenic pollen. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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