Evergreen needleleaf forests (ENFs) play a sizable role in the global carbon cycle, but the biological and physical controls on ENF carbon cycle feedback loops are poorly understood and difficult to measure. To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques designed to detect ENF photosynthetic activity with optical signals. This Overview summarizes how fundamental plant biological and biophysical processes control the fate of photons from leaf to globe, ultimately enabling remote estimates of ENF photosynthesis. We demonstrate this using data across four ENF sites spanning a broad range of environmental conditions and link leaf- and stand-scale observations of photosynthesis (i.e., needle biochemistry and flux towers) with tower- and satellite-based remote sensing. The multidisciplinary nature of this work can serve as a model for the coordination and integration of observations made at multiple scales.
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PREMISE Biological invasions increasingly threaten native biodiversity and ecosystem services. One notable example is the common reed,
Phragmites australis , which aggressively invades North American salt marshes. Elevated atmospheric CO2and nitrogen pollution enhance its growth and facilitate invasion becauseP. australis responds more strongly to these enrichments than do native species. We investigated how modifications to stomatal features contribute to strong photosynthetic responses to CO2and nitrogen enrichment inP. australis by evaluating stomatal shifts under experimental conditions and relating them to maximal stomatal conductance (g wmax) and photosynthetic rates.METHODS Plants were grown
in situ in open‐top chambers under ambient and elevated atmospheric CO2(eCO2) and porewater nitrogen (Nenr) in a Chesapeake Bay tidal marsh. We measured light‐saturated carbon assimilation rates (A sat) and stomatal characteristics, from which we calculatedg wmaxand determined whether CO2and Nenraltered the relationship betweeng wmaxandA sat.RESULTS eCO2and Nenrenhanced both
g wmaxandA sat, but to differing degrees;g wmaxwas more strongly influenced by Nenrthrough increases in stomatal density whileA satwas more strongly stimulated by eCO2. There was a positive relationship betweeng wmaxandA satthat was not modified by eCO2or Nenr, individually or in combination.CONCLUSIONS Changes in stomatal features co‐occur with previously described responses of
P. australis to eCO2and Nenr. Complementary responses of stomatal length and density to these global change factors may facilitate greater stomatal conductance and carbon gain, contributing to the invasiveness of the introduced lineage. -
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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 CO2flux 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.
