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Foliar resorption is a principal nutrient conservation mechanism in terrestrial vegetation that could be sensitive to ongoing changes in climate and atmospheric nitrogen (N) deposition. We quantified N resorption in northern hardwood forests along an elevation gradient of decreasing temperature and increasing soil N availability to evaluate how this critical nutrient cycling process can be expected to respond to global and regional environmental changes. Foliar N resorption proficiency (NRP) increased significantly at lower elevations for both sugar maple and American beech, the dominant species in these forests. Foliar N resorption efficiency (NRE) also decreased with increasing elevation, but only in one year. Both species exhibited strong negative relationships between NRP and soil N availability. Thus, we anticipate that with climate warming and decreasing N inputs, northern hardwood forests can be expected to exhibit stronger N conservation via foliar resorption. Both species also exhibited strong correlations between resorption efficiency of N and C, but resorption of both elements was much greater for beech than sugar maple, suggesting contrasting mechanisms of nutrient conservation between these two widespread species. 

Long-term streamflow datasets inevitably include gaps, which must be filled to allow estimates of runoff and ultimately catchment water budgets. Uncertainty introduced by filling gaps in discharge records is rarely, if ever, reported. We characterized the uncertainty due to streamflow gaps in a reference watershed at the Hubbard Brook Experimental Forest (HBEF) from 1996 to 2009 by simulating artificial gaps of varying duration and flow rate, with the objective of quantifying their contribution to uncertainty in annual streamflow. Gaps were filled using an ensemble of regressions relating discharge from nearby streams, and the predicted flow was compared to the actual flow. Differences between the predicted and actual runoff increased with both gap length and flow rate, averaging 2.8% of the runoff during the gap. At the HBEF, the sum of gaps averaged 22 days per year, with the lowest and highest annual uncertainties due to gaps ranging from 1.5 mm (95% confidence interval surrounding mean runoff) to 21.1 mm. As a percentage of annual runoff, uncertainty due to gap filling ranged from 0.2–2.1%, depending on the year. Uncertainty in annual runoff due to gaps was small at the HBEF, where infilling models are based on multiple similar catchments in close proximity to the catchment of interest. The method demonstrated here can be used to quantify uncertainty due to gaps in any long-term streamflow data set, regardless of the gap-filling model applied.