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Pest Management Science
Article
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Pest Management Science
Article . 2016 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
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Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology

Authors: Scott C, Merrill; Frank B, Peairs;

Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology

Abstract

AbstractBACKGROUNDModels describing the effects of climate change on arthropod pest ecology are needed to help mitigate and adapt to forthcoming changes. Challenges arise because climate data are at resolutions that do not readily synchronize with arthropod biology. Here we explain how multiple sources of climate and weather data can be synthesized to quantify the effects of climate change on pest phenology.RESULTSPredictions of phenological events differ substantially between models that incorporate scale‐appropriate temperature variability and models that do not. As an illustrative example, we predicted adult emergence of a pest of sunflower, the sunflower stem weevil Cylindrocopturus adspersus (LeConte). Predictions of the timing of phenological events differed by an average of 11 days between models with different temperature variability inputs. Moreover, as temperature variability increases, developmental rates accelerate.CONCLUSIONOur work details a phenological modeling approach intended to help develop tools to plan for and mitigate the effects of climate change. Results show that selection of scale‐appropriate temperature data is of more importance than selecting a climate change emission scenario. Predictions derived without appropriate temperature variability inputs will likely result in substantial phenological event miscalculations. Additionally, results suggest that increased temperature instability will lead to accelerated pest development. © 2016 Society of Chemical Industry

Related Organizations
Keywords

Climate Change, Temperature, Models, Theoretical, Animals, Helianthus, Weevils, Monte Carlo Method, Weather, Forecasting

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
hybrid