A statistical estimator for determining the limits of contemporary and historic phenology

Climate change affects not just where species are found, but also when species’ key life-history events occur—their phenol-

ogy. Measuring such changes in timing is often hampered by a reliance on biased survey data: surveys identify that an event

has taken place (for example, the flower is in bloom), but not when that event happened (for example, the flower bloomed

yesterday). Here, we show that this problem can be circumvented using statistical estimators, which can provide accurate and

unbiased estimates from sparsely sampled observations. We demonstrate that such methods can resolve an ongoing debate

about the relative timings of the onset and cessation of flowering, and allow us to place modern observations reliably within the

context of the vast wealth of historical data that reside in herbaria, museum collections, and written records. We also analyse

large-scale citizen science data from the United States National Phenology Network and reveal not just earlier but also poten-

tially more variable flowering in recent years. Evidence for greater variability through time is important because increases in

variation are characteristic of systems approaching a state change.