Evaluating confidence in climate‐based predictions of population change in a migratory species. Issue 8 (12th May 2016)

• Record Type: Journal Article
• Authors: Saunders, Sarah P.; Ries, Leslie; Oberhauser, Karen S.; Zipkin, Elise F.
• Other Names: Field Richard checker.
• Is Part Of: Global ecology & biogeography. Volume 25:Issue 8(2016)
• Page Start: 1000
• Page End: 1012
• Publication Date: 2016-05-12
• Subjects: Climate change -- Danaus plexippus -- forecasting -- migratory -- monarch butterflies -- negative binomial model -- prediction -- spatial synchrony; Ecology -- Periodicals; Biogeography -- Periodicals; Biodiversity -- Periodicals; Macroevolution -- Periodicals; 577
• DOI: 10.1111/geb.12461
• Languages: English
• ISSNs: 1466-822X

Abstract

Abstract: Aim: Forecasting ecological responses to climate change is a common objective, but there are few methods for evaluating confidence in such predictions. For migratory species, in particular, it is also essential to consider the extent of spatial synchrony among separate breeding populations in range‐wide predictions. We develop a quantitative method to evaluate the accuracy of climate‐based ecological predictions and use this approach to assess the extent of spatio‐temporal synchrony among distinct regions within the breeding range of a single migratory population. Location: We model weekly site‐specific summer abundances (1996–2011) of monarch butterflies ( Danaus plexippus ) in the Midwestern USA as a function of climate conditions experienced during a shared spring migration/breeding phase in Texas and separate summer recruitment periods in Ohio and Illinois. Methods: Using negative binomial regression models, we evaluate spatio‐temporal synchrony between monarchs in the two states and develop a novel quantitative assessment approach to determine the temporal predictive strength of our model with Bayesian P ‐values. Results: Monarchs breeding in the Midwest exhibit spatio‐temporal synchrony in Ohio and Illinois; cooler spring temperatures, average to above average precipitation in Texas and cooler than average summer temperatures are associated with higher population abundances in both states. At least 10 years of data are needed for adequate model predictability of average future counts. Because annual spring weather conditions in Texas primarily drive yearly abundances, as opposed to localized summer effects, year‐specific counts are often difficult to predict reliably, specifically when predictive spring conditions are outside the range of typical regional conditions. Main conclusions: Our assessment method can be used in similar analyses to more confidently interpret ecological responses to climate change. Our results demonstrate the relative importance of climatic drivers in predicting abundances of a migratory species and the difficulties in producing reliable predictions of animal populations in the face of climate change.