Search

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

• Patterns of summer forest productivity were examined in the context of winter snow and summer rain. • The GPP season was longer in years with earlier snowmelt. • Total GPP was higher for a longer GPP season in sites with wetter summer climate. • Total GPP was lower for a longer GPP season in sites with drier summer climate. • These patterns were weaker in forests with snow as a higher annual fraction of precipitation. Seasonal snow cover is important in shaping ecosystem carbon uptake across many regions of the world, however forest responses to projected declines in snowpack remain uncertain. We studied the response of forest gross primary productivity (GPP) during the photosynthetically active season to interannual and spatial variability in snow water equivalent (SWE), timing of snowmelt, and length of the active season. We combined carbon flux and weather data from 14 temperate deciduous and evergreen forests in the US and southeast Canada with SWE and precipitation from the Snow Data Assimilation System to test these hypotheses: 1) earlier snowmelt leads to a longer active season; 2) a longer active season is associated with higher total GPP, and 3) GPP during the active season is dependent on peak SWE and timing of snowmelt the previous winter. Regression and correlation analyses did not reveal meaningful environmental predictors of interannual variability in GPP, so linear mixed effects models were used to analyze broader scale spatiotemporal patterns. We found that active season length was negatively correlated with total active season GPP in forests with drier summers on average (based on mean annual summer climatic water deficit), but positively correlated in areas with typically wetter summers. The magnitude of these effects decreased at forests with a higher percentage of annual precipitation falling as snow. Our results showed that the capacity for plants to gain more carbon during a longer active season appears to be dependent on soil water status determined by long-term climate, rather than interannual fluctuations in weather. We found no evidence that the magnitude of total snowfall or peak SWE had a legacy effect on subsequent active season GPP. Finally, we highlight that there was large interannual variability both within and between sites that was not well explained by seasonal climate or phenology. [ABSTRACT FROM AUTHOR]

• There were fundamental differences in the light response of SIF and GPP • SIF responded to light earlier in the year than the spring onset of GPP • SIF did not exhibit a parallel response to temperature and moisture controls on GPP • In summer SIF was not correlated with canopy conductance The phenology of montane conifer forests is likely to shift in response to climate change and altered seasonal dynamics of light, temperature, and moisture. Solar-induced fluorescence (SIF) is expected to provide substantial improvement for mapping temporal changes in evergreen gross primary production (GPP) over greenness-based remote sensing indices. The utility of SIF to monitor seasonal changes in the phenology of conifer photosynthesis depends on the degree to which GPP and SIF respond in synchrony to key environmental drivers. However, to what extent SIF and GPP become decoupled by responding differently to the combined effects of light and other environmental conditions remains unknown. The goal of this study was to characterize the responses of GPP and SIF red to a suite of environmental drivers at the half-hour time scale and determine how these relationships change across seasons. We analyzed one year of tower-based SIF red and eddy covariance-derived GPP data from a conifer forest at Niwot Ridge, Colorado. We compared the light responses of GPP and SIF red across the year, finding that SIF red increased in response to light earlier in the year than did GPP. The light response of GPP had a positive temperature dependence in spring, and this dependency reversed in summer due to increased evaporative demand, while the light response of SIF red was less temperature dependent. Using artificial neural network ensemble analysis, we found that from spring to summer, SIF red did not exhibit a parallel response to the seasonally dynamic temperature and moisture controls on GPP. In summer SIF red was not correlated with canopy conductance, suggesting that SIF is less sensitive to stomatal control than GPP. Our results suggest that, in conifers, photosystems begin to activate in spring prior to when water becomes available for photosynthesis, presenting a challenge for the use of SIF as a phenological indicator in conifer forests. [ABSTRACT FROM AUTHOR]