Williams Mathew

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Williams
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Mathew
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  • Article
    Optical instruments for measuring leaf area index in low vegetation : application in Arctic ecosystems
    (Ecological Society of America, 2005-08) van Wijk, Mark T. ; Williams, Mathew
    Leaf area index (LAI) is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. We set out to develop a methodology for easy and accurate nondestructive assessment of the variability of LAI in low-stature vegetation. We developed and tested the methodology in an arctic landscape close to Abisko, Sweden. The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance (NDVI) and light penetration through the canopy (gap-fraction analysis using a LI-COR LAI-2000). By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained. The combination of an NDVI sensor for sparser vegetation and a LAI-2000 for denser vegetation could explain 81% of the variance of LAI measured by destructive harvest. We used the method to quantify the spatial variability and the associated uncertainty of leaf area index in a small catchment area.
  • 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.
  • Preprint
    Tight coupling between leaf area index and foliage N content in arctic plant communities
    ( 2004-09-17) van Wijk, Mark T. ; Williams, Mathew ; Shaver, Gaius R.
    The large spatial heterogeneity of arctic landscapes complicates efforts to quantify key processes of these ecosystems, for example productivity, at the landscape level. Robust relationships that help to simplify and explain observed patterns, are thus powerful tools for understanding and predicting vegetation distribution and dynamics. Here we present the same linear relationship between leaf area index and total foliar nitrogen, the two factors determining the photosynthetic capacity of vegetation, across a wide range of tundra vegetation types in both Northern-Sweden and Alaska between leaf area indices of 0 and 1 m2 m-2, which is essentially the entire range of leaf area index values for the Arctic as a whole. Surprisingly, this simple relationship arises as an emergent property at the plant community level, whereas at the species level a large variability in leaf traits exists. As the relationship between LAI and foliar N exists among such varied ecosystems, the arctic environment must impose tight constraints on vegetation canopy development. This relationship simplifies the quantification of vegetation productivity of arctic vegetation types as the two most important drivers of productivity can now be estimated reliably from remotely sensed NDVI images.
  • Preprint
    Incident radiation and the allocation of nitrogen within Arctic plant canopies : implications for predicting gross primary productivity
    ( 2012-01) Street, Lorna E. ; Shaver, Gaius R. ; Rastetter, Edward B. ; van Wijk, Mark T. ; Kaye, Brooke A. ; Williams, Mathew
    Arctic vegetation is characterized by high spatial variability in plant functional type (PFT) composition and gross primary productivity (P). Despite this variability, the two main drivers of P in sub-Arctic tundra are leaf area index (LT) and total foliar nitrogen (NT). LT and NT have been shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-scaling by allowing quantification of the main drivers of P from remotely sensed LT. Our objective was to test the LT–NT relationship across multiple Arctic latitudes and to assess LT as a predictor of P for the pan-Arctic. Including PFT-specific parameters in models of LT–NT coupling provided only incremental improvements in model fit, but significant improvements were gained from including site-specific parameters. The degree of curvature in the LT–NT relationship, controlled by a fitted canopy nitrogen extinction co-efficient, was negatively related to average levels of diffuse radiation at a site. This is consistent with theoretical predictions of more uniform vertical canopy N distributions under diffuse light conditions. Higher latitude sites had higher average leaf N content by mass (NM), and we show for the first time that LT–NT coupling is achieved across latitudes via canopy-scale trade-offs between NM and leaf mass per unit leaf area (LM). Site-specific parameters provided small but significant improvements in models of P based on LT and moss cover. Our results suggest that differences in LT–NT coupling between sites could be used to improve pan-Arctic models of P and we provide unique evidence that prevailing radiation conditions can significantly affect N allocation over regional scales.
  • Article
    Shallow soils are warmer under trees and tall shrubs across arctic and boreal ecosystems
    (IOP Publishing, 2020-12-18) Kropp, Heather ; Loranty, Michael M. ; Natali, Susan M. ; Kholodov, Alexander L. ; Rocha, Adrian V. ; Myers-Smith, Isla H. ; Abbott, Benjamin W. ; Abermann, Jakob ; Blanc-Betes, Elena ; Blok, Daan ; Blume-Werry, Gesche ; Boike, Julia ; Breen, Amy L. ; Cahoon, Sean M. P. ; Christiansen, Casper T. ; Douglas, Thomas A. ; Epstein, Howard E. ; Frost, Gerald V. ; Goeckede, Mathias ; Høye, Toke T. ; Mamet, Steven D. ; O’Donnell, Jonathan A. ; Olefeldt, David ; Phoenix, Gareth K. ; Salmon, Verity G. ; Sannel, A. Britta K. ; Smith, Sharon L. ; Sonnentag, Oliver ; Smith Vaughn, Lydia ; Williams, Mathew ; Elberling, Bo ; Gough, Laura ; Hjort, Jan ; Lafleur, Peter M. ; Euskirchen, Eugenie ; Heijmans, Monique M. P. D. ; Humphreys, Elyn ; Iwata, Hiroki ; Jones, Benjamin M. ; Jorgenson, M. Torre ; Grünberg, Inge ; Kim, Yongwon ; Laundre, James A. ; Mauritz, Marguerite ; Michelsen, Anders ; Schaepman-Strub, Gabriela ; Tape, Ken D. ; Ueyama, Masahito ; Lee, Bang-Yong ; Langley, Kirsty ; Lund, Magnus
    Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.