Severe drought can cause lagged effects on tree physiology that negatively impact forest functioning for years. These “drought legacy effects” have been widely documented in tree‐ring records and could have important implications for our understanding of broader scale forest carbon cycling. However, legacy effects in tree‐ring increments may be decoupled from ecosystem fluxes due to (a) postdrought alterations in carbon allocation patterns; (b) temporal asynchrony between radial growth and carbon uptake; and (c) dendrochronological sampling biases. In order to link legacy effects from tree rings to whole forests, we leveraged a rich dataset from a Midwestern US forest that was severely impacted by a drought in 2012. At this site, we compiled tree‐ring records, leaf‐level gas exchange, eddy flux measurements, dendrometer band data, and satellite remote sensing estimates of greenness and leaf area before, during, and after the 2012 drought. After accounting for the relative abundance of tree species in the stand, we estimate that legacy effects led to ~10% reductions in tree‐ring width increments in the year following the severe drought. Despite this stand‐scale reduction in radial growth, we found that leaf‐level photosynthesis, gross primary productivity (GPP), and vegetation greenness were not suppressed in the year following the 2012 drought. Neither temporal asynchrony between radial growth and carbon uptake nor sampling biases could explain our observations of legacy effects in tree rings but not in GPP. Instead, elevated leaf‐level photosynthesis co‐occurred with reduced leaf area in early 2013, indicating that resources may have been allocated away from radial growth in conjunction with postdrought upregulation of photosynthesis and repair of canopy damage. Collectively, our results indicate that tree‐ring legacy effects were not observed in other canopy processes, and that postdrought canopy allocation could be an important mechanism that decouples tree‐ring signals from GPP.
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.
more » « less- Award ID(s):
- 1929709
- NSF-PAR ID:
- 10484482
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- BioScience
- Volume:
- 74
- Issue:
- 3
- ISSN:
- 0006-3568
- Format(s):
- Medium: X Size: p. 130-145
- Size(s):
- p. 130-145
- Sponsoring Org:
- National Science Foundation
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Abstract The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite‐derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above‐ground biomass. The adjusted simulation significantly reduced the above‐ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation‐constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.
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Abstract Accurate estimation of terrestrial gross primary productivity (
GPP ) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF ) in a temperate deciduous forest at Harvard Forest, Massachusetts,USA . Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R 2 = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively;P < 0.0001). We developed a model to estimateGPP from the tower‐based measurement ofSIF and leaf‐level ChlF parameters. The estimation ofGPP from this model agreed well with flux tower observations ofGPP (R 2 = 0.68;P < 0.0001), demonstrating the potential ofSIF for modelingGPP . At the leaf scale, we found that leafF q ’ /F m ’ , the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopySIF yield (SIF /APAR ,R 2 = 0.79;P < 0.0001). We also found that canopySIF andSIF ‐derivedGPP (GPPSIF ) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R 2 = 0.65 for canopyGPPSIF and chlorophyll content;P < 0.0001), leaf area index (LAI ) (R 2 = 0.35 for canopyGPPSIF andLAI ;P < 0.0001), and normalized difference vegetation index (NDVI ) (R 2 = 0.36 for canopyGPPSIF andNDVI ;P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales. -
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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.
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Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (StartGPPand EndGPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO2to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d−1) than ENF (1.10% d−1), and their active season length (EndGPP–StartGPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in StartGPP, EndGPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier StartGPP(1.3–2.5 days °C−1) or later EndGPP(1.5–1.8 days °C−1, depending on forest type and month). Finally, we tested whether existing phenological models could predict StartGPPand EndGPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for StartGPP, while a chilling‐degree‐day model was best for EndGPP. The root mean square errors (RMSE) between predicted and observed StartGPPand EndGPPwere 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).
