Person: Keenan, Trevor
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Keenan
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Trevor
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Keenan, Trevor
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Publication Forest ecosystem changes from annual methane source to sink depending on late summer water balance(Wiley-Blackwell, 2014) Shoemaker, Julie K.; Keenan, Trevor; Hollinger, David Y.; Richardson, AndrewForests dominate the global carbon cycle, but their role in methane (CH4) biogeochemistry remains uncertain. We analyzed whole-ecosystem CH4 fluxes from 2 years, obtained over a lowland evergreen forest in Maine, USA. Gross primary productivity provided the strongest correlation with the CH4 flux in both years, with an additional significant effect of soil moisture in the second, drier year. This forest was a neutral to net source of CH4 in 2011 and a small net sink in 2012. Interannual variability in the summer hydrologic cycle apparently shifts the ecosystem from being a net source to a sink for CH4. The small magnitude of the CH4 fluxes and observed control or CH4 fluxes by forest productivity and summer precipitation provide novel insight into the CH4 cycle in this globally important forest ecosystem.Publication Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment(Ecological Society of America, 2014) Keenan, Trevor; Darby, B.; Felts, Libby; Sonnentag, O.; Friedl, M. A.; Hufkens, K.; O'keefe, John; Klosterman, Stephen; Munger, J.; Toomey, M.; Richardson, AndrewDigital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.Publication Terrestrial Biosphere Model Performance for Inter-Annual Variability of Land-Atmosphere CO2 Exchange(Wiley-Blackwell, 2012) Keenan, Trevor; Baker, Ian; Barr, Alan; Ciais, Philippe; Dietze, Michael; Dragoni, Danillo; Gough, Christopher; Grant, Robert; Hollinger, David; Hufkens, Koen; Poulter, Ben; McCaughey, Harry; Racza, Brett; Ryu, Youngryel; Schaefer, Kevin; Tian, Hanqin; Verbeeeck, Hans; Zhao, Maosheng; Richardson, AndrewInterannual variability in biosphere-atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.Publication Using Model-Data Fusion to Interpret Past Trends, and Quantify Uncertainties in Future Projections, of Terrestrial Ecosystem Carbon Cycling(Wiley-Blackwell, 2012) Keenan, Trevor; Davidson, Eric; Moffat, Antje; Munger, William; Richardson, AndrewUncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model-data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy-covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process-based carbon cycle model, both against the measured decadal trend and under future long-term climate change. The use of high-frequency eddy-covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long-term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data-informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long-term trend. Furthermore, we use the model-data fusion framework, and the ANN, to show that the long-term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model-data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data-informed process-based model.Publication Circadian Control of Global Isoprene Emissions(Nature Publishing Group, 2012) Keenan, Trevor; Niinemets, ÜCircadian rhythms govern many aspects of our lives, and it is thought that earth-atmosphere interactions are similarly affected. In this correspondence, we show that reported circadian rhythms can be explained by a variety of mechanisms, which we are far from disentangling.Publication Rate My Data: Quantifying the Value of Ecological Data for Models of Terrestrial Carbon Cycle(Ecological Society of America, 2013) Keenan, Trevor; Davidson, Eric; Munger, James; Richardson, AndrewPrimarily driven by concern about rising levels of atmospheric CO2, ecologists and earth system scientists are collecting vast amounts of data related to the carbon cycle. These measurements are generally time-consuming and expensive to make, and, unfortunately, we live in an era where research funding is increasingly hard to come by. Thus, important questions are: ‘Which data streams provide the most valuable information? ’ and, ‘How much data do we need? ’ These questions are relevant not only for model developers, who need observational data to improve, constrain and test their models, but also for experimentalists and those designing ecological observation networks. Here we address these questions using a model-data fusion approach. We constrain a process-oriented, forest ecosystem C cycle model with seventeen different data streams from the Harvard Forest. We iteratively rank each data source according to its contribution to reducing model uncertainty. Results show the importance of some measurements commonly unavailable to carbon cycle modelers, such as estimates of turnover times from different carbon pools. Surprisingly, many data sources are relatively redundant in the presence of others, and do not lead to a significant improvement in model performance. A few select data sources lead to the largest reduction in parameter based model uncertainty. Projections of future carbon cycling were poorly constrained when only hourly net ecosystem exchange measurements were used to inform the model. They were well constrained, however, with only five of the seventeen data streams, even though many individual parameters are not constrained. The approach taken here should stimulate further cooperation between modelers and measurement teams, and may be useful in the context of setting research priorities and allocating research funds.Publication Measures of Light in Studies on Light-Driven Plant Plasticity in Artificial Environments(Frontiers Research Foundation, 2012) Niinemets, Ülo; Keenan, TrevorWithin-canopy variation in light results in profound canopy profiles in foliage structural, chemical, and physiological traits. Studies on within-canopy variations in key foliage traits are often conducted in artificial environments, including growth chambers with only artificial light, and greenhouses with and without supplemental light. Canopy patterns in these systems are considered to be representative to outdoor conditions, but in experiments with artificial and supplemental lighting, the intensity of artificial light strongly deceases with the distance from the light source, and natural light intensity in greenhouses is less than outdoors due to limited transmittance of enclosure walls. The implications of such changes in radiation conditions on canopy patterns of foliage traits have not yet been analyzed. We developed model-based methods for retrospective estimation of distance vs. light intensity relationships, for separation of the share of artificial and natural light in experiments with combined light and for estimation of average enclosure transmittance, and estimated daily integrated light at the time of sampling (Qint,C), at foliage formation (Qint,G), and during foliage lifetime (Qint,av). The implications of artificial light environments were analyzed for altogether 25 studies providing information on within-canopy gradients of key foliage traits for 70 species × treatment combinations. Across the studies with artificial light, Qint,G for leaves formed at different heights in the canopy varied from 1.8- to 6.4-fold due to changing the distance between light source and growing plants. In experiments with combined lighting, the share of natural light at the top of the plants varied threefold, and the share of natural light strongly increased with increasing depth in the canopy. Foliage nitrogen content was most strongly associated with Qint,G, but photosynthetic capacity with Qint,C, emphasizing the importance of explicit description of light environment during foliage lifetime. The reported and estimated transmittances of enclosures varied between 0.27 and 0.85, and lack of consideration of the reduction of light compared with outdoor conditions resulted in major underestimation of foliage plasticity to light. The study emphasizes that plant trait vs. light relationships in artificial systems are not directly comparable to natural environments unless modifications in lighting conditions in artificial environments are taken into account.