Short-term light and leaf photosynthetic dynamics affect estimates of daily understory photosynthesis in four tree species.

Instantaneous measurements of photosynthesis are often implicitly or explicitly scaled to longer time frames to provide an understanding of plant performance in a given environment. For plants growing in a forest understory, results from photosynthetic light response curves in conjunction with diurnal light data are frequently extrapolated to daily photosynthesis (A(day)), ignoring dynamic photosynthetic responses to light. In this study, we evaluated the importance of two factors on A(day) estimates: dynamic physiological responses to photosynthetic photon flux density (PPFD); and time-resolution of the PPFD data used for modeling. We used a dynamic photosynthesis model to investigate how these factors interact with species-specific photosynthetic traits, forest type, and sky conditions to affect the accuracy of A(day) predictions. Increasing time-averaging of PPFD significantly increased the relative overestimation of A(day) similarly for all study species because of the nonlinear response of photosynthesis to PPFD (15% with 5-min PPFD means). Depending on the light environment characteristics and species-specific dynamic responses to PPFD, understory tree A(day) can be overestimated by 6-42% for the study species by ignoring these dynamics. Although these overestimates decrease under cloudy conditions where direct sunlight and consequently understory sunfleck radiation is reduced, they are still significant. Within a species, overestimation of A(day) as a result of ignoring dynamic responses was highly dependent on daily sunfleck PPFD and the frequency and irradiance of sunflecks. Overall, large overestimates of A(day) in understory trees may cause misleading inferences concerning species growth and competition in forest understories with < 2% full sunlight. We conclude that comparisons of A(day) among co-occurring understory species in deep shade will be enhanced by consideration of sunflecks by using high-resolution PPFD data and understanding the physiological responses to sunfleck variation.