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In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1–3.2 days decade−1) compared to the spring (0.9–1.5 days decade−1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling. [ABSTRACT FROM AUTHOR]

Many secondary deciduous forests of eastern North America are approaching a transition in which mature early‐successional trees are declining, resulting in an uncertain future for this century‐long carbon (C) sink. We initiated the Forest Accelerated Succession Experiment (FASET) at the University of Michigan Biological Station to examine the patterns and mechanisms underlying forest C cycling following the stem girdling‐induced mortality of >6,700 early‐successional Populus spp. (aspen) and Betula papyrifera (paper birch). Meteorological flux tower‐based C cycling observations from the 33‐ha treatment forest have been paired with those from a nearby unmanipulated forest since 2008. Following over a decade of observations, we revisit our core hypothesis: that net ecosystem production (NEP) would increase following the transition to mid‐late‐successional species dominance due to increased canopy structural complexity. Supporting our hypothesis, NEP was stable, briefly declined, and then increased relative to the control in the decade following disturbance; however, increasing NEP was not associated with rising structural complexity but rather with a rapid 1‐yr recovery of total leaf area index as mid‐late‐successional Acer, Quercus, and Pinus assumed canopy dominance. The transition to mid‐late‐successional species dominance improved carbon‐use efficiency (CUE = NEP/gross primary production) as ecosystem respiration declined. Similar soil respiration rates in control and treatment forests, along with species differences in leaf physiology and the rising relative growth rates of mid‐late‐successional species in the treatment forest, suggest changes in aboveground plant respiration and growth were primarily responsible for increases in NEP. We conclude that deciduous forests transitioning from early to middle succession are capable of sustained or increased NEP, even when experiencing extensive tree mortality. This adds to mounting evidence that aging deciduous forests in the region will function as C sinks for decades to come. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

Abstract: We determined key environmental and meteorological drivers that influence the magnitude of the fluxes of carbon dioxide, latent heat, and sensible heat using data from different vegetation types at 56 sites of the AmeriFlux network in combination with a self-organizing feature map neural network. The technique was combined with a subsequent k-means clustering procedure to classify the turbulent fluxes. This method is a direct approach to assess the importance of several environmental parameters for the turbulent exchange rates above vegetated areas, based on eddy covariance measurements. The synthesis of the available dataset was achieved by merging 225 pre-clusters of fluxes that were found by the SOFM into 9 final clusters exhibiting mean CO2 fluxes from −5.8μmolm−2 s−1 to 3.8μmolm−2 s−1. The spatial and temporal comprehensive dataset covering 305 site years, combined with the independence from mechanistic ecological assumptions of the SOFM network approach provides an opportunity to assess the strength of the influence of various environmental parameters on the turbulent fluxes over vegetated areas. After ranking the environmental and meteorological variables according to their cluster separation strength, the pattern of turbulent fluxes turned out to be three times more sensitive to photosynthetic photon flux density and vegetation type than air temperature. Furthermore, the results indicate a strong influence of the water vapor deficit on the magnitude of the turbulent exchange. [Copyright &y& Elsevier]