Rising atmospheric [CO2 ], ca , is expected to affect stomatal regulation of leaf gas-exchange of woody plants, thus influencing energy fluxes as well as carbon (C), water, and nutrient cycling of forests. Researchers have proposed various strategies for stomatal regulation of leaf gas-exchange that include maintaining a constant leaf internal [CO2 ], ci , a constant drawdown in CO2 (ca  - ci ), and a constant ci /ca . These strategies can result in drastically different consequences for leaf gas-exchange. The accuracy of Earth systems models depends in part on assumptions about generalizable patterns in leaf gas-exchange responses to varying ca . The concept of optimal stomatal behavior, exemplified by woody plants shifting along a continuum of these strategies, provides a unifying framework for understanding leaf gas-exchange responses to ca . To assess leaf gas-exchange regulation strategies, we analyzed patterns in ci inferred from studies reporting C stable isotope ratios (δ(13) C) or photosynthetic discrimination (∆) in woody angiosperms and gymnosperms that grew across a range of ca spanning at least 100 ppm. Our results suggest that much of the ca -induced changes in ci /ca occurred across ca spanning 200 to 400 ppm. These patterns imply that ca  - ci will eventually approach a constant level at high ca because assimilation rates will reach a maximum and stomatal conductance of each species should be constrained to some minimum level. These analyses are not consistent with canalization toward any single strategy, particularly maintaining a constant ci . Rather, the results are consistent with the existence of a broadly conserved pattern of stomatal optimization in woody angiosperms and gymnosperms. This results in trees being profligate water users at low ca , when additional water loss is small for each unit of C gain, and increasingly water-conservative at high ca , when photosystems are saturated and water loss is large for each unit C gain.

Students in master's‐level courses in group work were surveyed regarding their attitudes toward the use of an experiential group activity as a component of their training, as well as the level of instructor involvement in these experiential groups. Students agreed that the experiential group was an effective and necessary teaching tool; however, a substantial minority indicated discomfort with the activity. Also, although the majority of instructors did not directly lead the experiential group, most either observed or received feedback on participation.

Watershed Boundary Dataset (WBD) 3D Elevation Program digital elevation model (DEM) NHDPlus High Resolution (NHDPlus HR)-A Hydrography Framework for the Nation High Resolution (NHDPlus HR), the next generation of NHDPlus updated, high-resolution datasets to create a modern, scalable, and openly accessible hydrography framework for the inland waters of the Nation. NHDPlus HR is built by integrating high-resolution National Hydrography Dataset (NHD) and Watershed Boundary Dataset (WBD) data with 3D Elevation Program (3DEP) 10-meter digital elevation model (DEM) data into a suite of vector, raster, and tabular datasets.NHDPlus HR datasets include the following:• Snapshots of the NHD, WBD, and DEM data used to build NHDPlus HR.• An elevation-based catchment (drainage) area for every flowline in the stream network, delivered in raster and vector formats.• Elevation-based flow direction and flow accumulation rasters, and the elevation data used to calculate them.• Tables containing value-added attributes that enhance stream network navigation, analysis, and display. NHDPlus HR Adds ValueNHDPlus HR value-added attributes (VAAs) enhance navigation and analysis functionality of the core NHD stream network. VAAs include hydrologic sequencing; stream order; cumulative drainage area; flow withdrawals, transfers, and augmentation; elevations and slopes for network flowlines; and mean annual flow and velocity estimates for each flowline. More details are available on the VAA web page (https://www.usgs. gov/NatHydroVAAs).

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Data is used to generate Fig. S1, which includes densities of rosettes and mature plants from 2004-2014 /*Data are rosettes, plotid (plot identification), counts, and quadrats. There are two woods each with two blocks. Quadno are quadrat numbers, numbers 1-11 indicate sample years from 2004-2014. Codes are SAS for PROC GLIMMIX.*/