A Bayesian Model of Citizen Science Data for Monitoring Environments Stressed by Climate Change

2815 

Contributed Abstract 

Paper 

Jonathan Auerbach (1), Theresa M. Crimmins (2), David Kepplinger (1), Ruishan Lin (1), E.M. Wolkovich (3)

(1) George Mason University, N/A, (2) University of Arizona, N/A, (3) University of British Columbia, N/A

Theresa M. Crimmins  
University of Arizona

David Kepplinger  
George Mason University

Ruishan Lin  
George Mason University

E.M. Wolkovich  
University of British Columbia

Jonathan Auerbach  
George Mason University

Ruishan Lin  
George Mason University

We propose a new method to adjust for the bias that occurs when citizen scientists monitor a fixed location and report whether an event of interest has occurred or not, such as whether a plant has bloomed. The bias arises as monitors note whether the event has happened upon arrival, lacking the precise day of occurrence. Adjustment is important because differences in monitoring patterns can make local environments appear more or less anomalous than they actually are, and the bias may persist when the data are aggregated across space or time. To correct for this bias, we propose a nonparametric Bayesian model that uses monotonic splines to estimate the distribution of bloom dates at different sites. We then use our model to determine whether the lilac monitored by citizen scientists in the northeast US bloomed anomalously early or late, preliminary evidence of environmental stress caused by climate change. Our analysis suggests that failing to correct for monitoring bias would underestimate the peak bloom date by 32 days on average. In addition, after adjusting for monitoring bias, several locations have anomalously early bloom dates that did not appear anomalous before adjustment.

nonparametric Bayes|monotonic splines|monitoring bias|bias correction|crowdsourcing|climate change

Section on Statistics and the Environment

Climate and Meteorology