AbstractClimate change leads to an increased frequency of severe weather events as well as stressful growing conditions. Together these changes may impact the resilience of ecosystems. To keep track of such effects, conservation managers monitor the “ecological integrity” or coherence of ecosystem processes, such as the cycling of carbon and water. Networked phenocams can produce near‐continuous observations of leaf function in the context of climate change, capturing declines due to disturbance or stress. Here we explore the application of phenocams to detect responses to disturbance and stress using 14 examples from the PhenoCam Network. We selected these previously published and new examples to include a variety of disturbances in the form of hurricanes, a windstorm, frost, insect defoliation, and stress due to drought. Frost and herbivory disturbances led to both reductions and extensions in the duration of the rising section of the greenness curve, while hurricanes generally led to reductions in the duration of the plateau section and entire leaf‐on period. We found that changes of at least ±20% in the duration of the rising section in the seasonal greenness curve, ±20% in the duration of the plateau section following the seasonal greenness peak, and ±10% in the duration of the entire leaf‐on period were a reliable signal of leaf functional declines due to disturbance or stress. If such declines become increasingly frequent and severe as a consequence of climate change, this could impact ecological integrity through interruptions to ecosystem processes. Comparing the duration of these periods in a given year to the average for other years with these thresholds resulted in average true detection rates of 86% and false‐positive detection rates of 11% when sampling from probability density functions of 344 broadleaf and needleleaf PhenoCam site‐years. Here we show that phenocams are powerful ecological integrity monitoring tools, which can be efficiently applied to quantify dynamic responses to disturbance or stress.