Abstract

Nitrogen has long been recognized as the most commonly limiting nutrient for plant production throughout the world. Yet, air pollution has created a modern chemical climate that has sometimes resulted in excess ecosystem N due to N deposition. In addition, climate warming could accelerate N cycling and N export from forested ecosystems. The result is increasing interest in understanding forest ecosystem N dynamics. This study used recently delineated climatic regions in Maine to investigate the possible influences of forest species composition, and energy and moisture gradients, on laboratory indices of forest floor N cycling. Concentrations of N and C, and potential net nitrification, potential net ammonification, and potential net N mineralization, were measured on forest floor samples from 20 sites distributed across Maine in both hardwood and softwood stands. Both forest types had nearly identical concentrations of N in the forest floor (∼1.6%), but the mean C/N ratio (28) under softwoods was significantly higher than that under hardwoods (24) due to higher concentrations of total C in soils under conifers. Forest floor N concentration was a better predictor of potential net N mineralization than was total C or C/N ratio. Although the most northerly region in this study was predictably the coldest, it was also the region with the highest values for total N and N cycling indices. Wet N deposition for the region indicates N deposition differences are not responsible for this spatial pattern, and further work is warranted to explain these results. Laboratory incubation measures of potential net N mineralization were significantly correlated with in situ annual net N mineralization, which supports the use of these techniques for forest soil N status evaluations. Most site measures of mean temperatures were negatively correlated with soil N indices indicating that warmer sites had lower rates of N cycling. Although differences existed in forest floor N characteristics between climate regions, they could not be predicted by simple relationships with temperature.

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