arXiv subjects

E. M. Wolkovich

Publications and source records attributed to E. M. Wolkovich.

2 recordsLinked to original sources

arXiv2026

Scattered spring: How climate change disrupts the synchrony of biological events

Many biological processes, including plant leafout and flowering, occur once cumulative temperatures reach a threshold, a relationship known as the thermal-sum model. In this way, temperature is thought to coordinate the timing of biological events. An important implication is that higher temperatures cause thresholds to be reached sooner so that the timing of spring events, for example, should advance earlier in the calendar year as climates warm. But growing evidence has found that, as climates have warmed, the rate of advancement has slowed (a trend known as declining sensitivity), while the variance in the timing of spring events has increased in many cases (a trend we call declining synchrony). These trends raise questions about the resilience of temperature-based coordination to anthropogenic climate change. To answer these questions, researchers have modified the thermal-sum model by introducing additional factors and mechanisms, such as chilling and photoperiod. We show such complexity is not necessary to explain current trends of sensitivity and synchrony. Using experimental and real-world data, we find these trends are exactly as predicted by the thermal-sum model. In particular, the model predicts that as temperatures continue to increase and springtime events shift from the equinox toward the winter solstice, those events will become less synchronized and more variable, a phenomenon we refer to as a scattered spring. By 2100, our results predict much of the American South will experience a scattered spring under a high-warming scenario, with the average time between spring flowering events increasing by several weeks in many locations.

stat.AP↗

arXiv2025

A Nonparametric Bayesian Model to Adjust for Monitoring Bias with an Application to Identifying Environments Stressed by Climate Change

We propose a new method to adjust 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 is in flower or not. The goal is to estimate the time the event occurred at that location. The problem is that popular estimators often incur bias both 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 propose a nonparametric Bayesian model that uses monotonic splines to estimate the event time. We first demonstrate the problem and our proposed solution using simulated data. We then apply our method to a real-world example from phenology in which lilac are monitored by citizen scientists in the northeastern United States, and the timing of the flowering is used to study anthropogenic warming. Our analysis suggests that common 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. Our findings underscore the importance of accounting for monitoring bias in event-time estimation. By applying our nonparametric Bayesian model with monotonic splines, we provide a more accurate approach to estimating bloom dates, revealing previously undetected anomalies and improving the reliability of citizen science data for environmental monitoring.

stat.AP↗