An Adjustment for Monitoring Bias with Application to Identifying Environments Stressed by Climate Change

We propose an adjustment for the bias that occurs when an individual monitors a location and reports the status of an event. For example, a monitor may visit a plant each week and report whether the plant has flowered or not. The goal is to estimate the time the event occurred at that location. The problem is that popular estimators often incur bias because the event may not coincide with the arrival of the monitor and because the monitor may report the status in error. To correct for this bias, we use monotonic splines to estimate the event time. We first demonstrate the problem and our proposed solution. We then apply our method to a real-world example from phenology in which lilac are monitored by citizen scientists across the Northeastern USA, and the timing of the flowering is used to study anthropogenic warming. Our analysis suggests that current methods fail to account for monitoring bias and underestimate the peak bloom date of the lilac by 48 days on average. In addition, after adjusting for monitoring bias, several locations had anomalously late bloom dates that did not appear anomalous before adjustment. Supplementary materials accompanying this paper appear on-line.