title: Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology.
abstract: BACKGROUND
  Models 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.
  
  
  RESULTS
  Predictions 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.
  
  
  CONCLUSIONS
  Our 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.
authors.affiliation: Department of Plant and Soil Science, University of Vermont, Burlington, VT, USA.
authors.forename: Scott C
authors.initials: SC
authors.lastname: Merrill
authors.affiliation: Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA.
authors.forename: Frank B
authors.initials: FB
authors.lastname: Peairs
mesh_terms: D000818:Animals; D057231:Climate Change; D005544:Forecasting; D006368:Helianthus / Q000469:parasitology; D008962:Models, Theoretical; D009010:Monte Carlo Method; D013696:Temperature; D014887:Weather; D048889:Weevils / Q000254:growth & development*
keywords: Cylindrocopturus adspersus; Kaufmann effect; non-linear modeling; spatiotemporal model; sunflower stem weevil; variance signal