Hobbie John E.

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Hobbie
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John E.
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Author: Kling, George W. × Type: Article ×

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  • Article
    Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter
    (Ecological Society of America, 2010-07) Rastetter, Edward B. ; Williams, Mathew ; Griffin, Kevin L. ; Kwiatkowski, Bonnie L. ; Tomasky, Gabrielle ; Potosnak, Mark J. ; Stoy, Paul C. ; Shaver, Gaius R. ; Stieglitz, Marc ; Hobbie, John E. ; Kling, George W.
    Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.
  • Article
    Bacterioplankton community shifts in an Arctic lake correlate with seasonal changes in organic matter source
    (American Society for Microbiology, 2003-04) Crump, Byron C. ; Kling, George W. ; Bahr, Michele ; Hobbie, John E.
    Seasonal shifts in bacterioplankton community composition in Toolik Lake, a tundra lake on the North Slope of Alaska, were related to shifts in the source (terrestrial versus phytoplankton) and lability of dissolved organic matter (DOM). A shift in community composition, measured by denaturing gradient gel electrophoresis (DGGE) of 16S rRNA genes, occurred at 4°C in near-surface waters beneath seasonal ice and snow cover in spring. This shift was associated with an annual peak in bacterial productivity ([14C]leucine incorporation) driven by the large influx of labile terrestrial DOM associated with snow meltwater. A second shift occurred after the flux of terrestrial DOM had ended in early summer as ice left the lake and as the phytoplankton community developed. Bacterioplankton communities were composed of persistent populations present throughout the year and transient populations that appeared and disappeared. Most of the transient populations could be divided into those that were advected into the lake with terrestrial DOM in spring and those that grew up from low concentrations during the development of the phytoplankton community in early summer. Sequencing of DNA in DGGE bands demonstrated that most bands represented single ribotypes and that matching bands from different samples represented identical ribotypes. Bacteria were identified as members of globally distributed freshwater phylogenetic clusters within the {alpha}- and ß-Proteobacteria, the Cytophaga-Flavobacteria-Bacteroides group, and the Actinobacteria.
  • Article
    Ecosystem responses to climate change at a Low Arctic and a High Arctic long-term research site
    (Springer, 2017-01-23) Hobbie, John E. ; Shaver, Gaius R. ; Rastetter, Edward B. ; Cherry, Jessica E. ; Goetz, Scott J. ; Guay, Kevin C. ; Gould, William A. ; Kling, George W.
    Long-term measurements of ecological effects of warming are often not statistically significant because of annual variability or signal noise. These are reduced in indicators that filter or reduce the noise around the signal and allow effects of climate warming to emerge. In this way, certain indicators act as medium pass filters integrating the signal over years-to-decades. In the Alaskan Arctic, the 25-year record of warming of air temperature revealed no significant trend, yet environmental and ecological changes prove that warming is affecting the ecosystem. The useful indicators are deep permafrost temperatures, vegetation and shrub biomass, satellite measures of canopy reflectance (NDVI), and chemical measures of soil weathering. In contrast, the 18-year record in the Greenland Arctic revealed an extremely high summer air-warming of 1.3°C/decade; the cover of some plant species increased while the cover of others decreased. Useful indicators of change are NDVI and the active layer thickness.
  • Article
    Biogeography of bacterioplankton in lakes and streams of an arctic tundra catchment
    (Ecological Society of America, 2007-06) Crump, Byron C. ; Adams, Heather E. ; Hobbie, John E. ; Kling, George W.
    Bacterioplankton community composition was compared across 10 lakes and 14 streams within the catchment of Toolik Lake, a tundra lake in Arctic Alaska, during seven surveys conducted over three years using denaturing gradient gel electrophoresis (DGGE) of PCR-amplified rDNA. Bacterioplankton communities in streams draining tundra were very different than those in streams draining lakes. Communities in streams draining lakes were similar to communities in lakes. In a connected series of lakes and streams, the stream communities changed with distance from the upstream lake and with changes in water chemistry, suggesting inoculation and dilution with bacteria from soil waters or hyporheic zones. In the same system, lakes shared similar bacterioplankton communities (78% similar) that shifted gradually down the catchment. In contrast, unconnected lakes contained somewhat different communities (67% similar). We found evidence that dispersal influences bacterioplankton communities via advection and dilution (mass effects) in streams, and via inoculation and subsequent growth in lakes. The spatial pattern of bacterioplankton community composition was strongly influenced by interactions among soil water, stream, and lake environments. Our results reveal large differences in lake-specific and stream-specific bacterial community composition over restricted spatial scales (<10 km) and suggest that geographic distance and connectivity influence the distribution of bacterioplankton communities across a landscape.