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Kim, Youngil

Publications and source records attributed to Kim, Youngil.

No evidence of canopy-scale leaf thermoregulation to cool leaves below air temperature across a range of forest ecosystems

Understanding and predicting the relationship between leaf temperature (T leaf ) and air temperature (T air ) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime T leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T air at higher temperatures (i.e., > ~25–30°C) leading to slopes <1 in T leaf /T air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (T can ) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to T can /T air slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the T can /T air relationship. Canopy structure also plays an important role in T can dynamics. Future climate warming is likely to lead to even greater T can , with attendant impacts on forest carbon cycling and mortality risk.

59 BASIC BIOLOGICAL SCIENCES↗

Investigating old-growth ponderosa pine physiology using tree-rings, δ 13 C, δ 18 O, and a process-based model

In dealing with predicted changes in environmental conditions outside those experienced today, forest managers and researchers rely on process-based models to inform physiological processes and predict future forest growth responses. The carbon and oxygen isotope ratios of tree-ring cellulose (d13Ccell, d18Ocell) reveal long-term, integrated physiological responses to environmental conditions. We incorporated a submodel of d18Ocell into the widely used Physiological Principles in Predicting Growth (3-PG) model for the first time, to complement a recently added d13Ccell submodel. We parameterized the model using previously reported stand characteristics and long-term trajectories of tree-ring growth, d13Ccell, and d18Ocell collected from the Metolius AmeriFlux site in central Oregon (upland trees). We then applied the parameterized model to a nearby set of riparian trees to investigate the physiological drivers of differences in observed basal area increment (BAI) and d13Ccell trajectories between upland and riparian trees. The model showed that greater available soil water and maximum canopy conductance likely explain the greater observed BAI and lower d13Ccell of riparian trees. Unexpectedly, both observed and simulated d18Ocell trajectories did not differ between the upland and riparian trees, likely due to similar d18O of source water isotope composition. The d18Ocell submodel with a Peclet effect improved model estimates of d18Ocell because its calculation utilizes 3-PG growth and allocation processes. Because simulated stand-level transpiration (E) is used in the d18O submodel, aspects of leaf-level anatomy such as the effective path length for transport of water from the xylem to the sites of evaporation could be estimated.

54 ENVIRONMENTAL SCIENCES↗

Selecting the Best Wild Card Entries in a Covering Array

For some covering arrays, there are some wild card positions in the array where there is flexibility about what factor levels can be chosen with no impact on the basic properties of covering array, because all of the required pairs have been covered. The choice of how to fill these wild card positions can influence other properties, such as the degrees of orthogonality or three-way coverage of the array. Here in this article, some criteria are proposed for identifying the best choices for the wild card positions to create covering arrays with highly desirable properties. Accompanying graphical summaries are also described to highlight differing performance for several examples.

97 MATHEMATICS AND COMPUTING↗

Graphical methods for the sensitivity analysis in discriminant analysis

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern of the change.

97 MATHEMATICS AND COMPUTING↗

Graphical methods for evaluating covering arrays

Covering arrays relax the condition of orthogonal arrays by only requiring that all combination of levels be covered but not requiring that the appearance of all combination of levels be balanced. This allows for a much larger number of factors to be simultaneously considered but at the cost of poorer estimation of the factor effects. To better understand patterns between sets of columns and evaluate the degree of coverage to compare and select between alternative arrays, we suggest several new graphical methods that show some of the patterns of coverage for different designs. As a result, these graphical methods for evaluating covering arrays are illustrated with a few examples.

97 MATHEMATICS AND COMPUTING↗