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Landscape Patterns of Net Nitrification in a Northern Hardwood-Conifer Forest

Rodney T. Venterea*,a, Gary M. Lovetta, Peter M. Groffmana and Paul A. Schwarzb

a Institute of Ecosystem Studies, P.O. Box AB, Millbrook, NY 12545
b Dep. of Forest Science, Oregon State Univ., Corvallis, OR, 97331



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Fig. 1. Representation of Hubbard Brook Experimental Forest, showing 100 plot locations utilized in present study. Numbers are plot designations previously established by Schwarz et al. (2001). Dashed lines are approximate boundaries of areas used in previous watershed-scale studies (Likens and Bormann, 1995).

 


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Fig. 2. Net nitrification rates versus NO-3–N concentrations determined in soils stored at 4°C for 1 to 3 d following field collection for 100 plots distributed across the Hubbard Brook Experimental Forest.

 


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Fig. 3. Net nitrification rates (NR) per mass soil in (a) mineral and (b) organic horizon soils versus NR predicted from multiple regression models in the form of Eq. [1] with {phi} = 2. Regression coefficients (ai) shown for each independent variable (Ai) in order of decreasing importance to overall model. {theta}r = relative water content; C/N = kg C per kg N; Mr = net N mineralization rate (mg N kg-1 d-1); N = g N per 100 g soil; C = g C per 100 g soil. * signifies p < 0.05; ** signifies p < 0.01; *** signifies p < 0.001.

 


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Fig. 4. Net nitrification rates (NR) and NO-3–N concentrations determined in soils stored at 4°C for 1 to 3 d following field collection in plots comprised of 50% or more basal area of a single species. RS = red spruce; BF = balsam fir; AB = American beech; YB = yellow birch; SM = sugar maple; PB = paper birch. Species with the same letter designations for each parameter are not significantly different (p > 0.05), using least significant differences multiple range test. Coefficients of variation (CV) values are tabulated.

 


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Fig. 5. Net nitrification rates (NR) on an areal basis versus NR predicted from multiple regression models in the form of Eq. [1] with {phi} = 2, using (a) landscape factors, (b) landscape factors and soil chemical properties, and (c) landscape factors, soil chemical properties, and microbial process rates. Regression coefficients (ai) shown for each independent variable (Ai) in order of decreasing importance to overall model. El = elevation (m); S = Southness; SM = sugar maple basal area; StM = striped maple basal area; CON = conifer basal area (m2 ha-1); C/N = kg C kg -1 N on areal basis; Mr = net N mineralization rate (kg N ha-1 d-1); * signifies p < 0.05; **signifies p < 0.01; *** signifies p < 0.001.

 


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Fig. 6. Measured NO-3concentrations on an areal basis versus NO-3 predicted from multiple regression models in the form of Eq. [1] with {phi} = 2, using (a) landscape factors, (b) landscape factors and soil chemical properties, and (c) landscape factors, soil chemical properties, and microbial process rates. Regression coefficients (ai) shown for each independent variable (Ai) in order of decreasing importance to overall model. El = elevation (m); S = Southness; BF = balsam fir basal area; StM = striped maple basal area; BA = total basal area (m2 ha-1); C/N = kg C kg -1 N on areal basis; Mr = net N mineralization rate (kg N ha-1 d-1); * signifies p < 0.05; ** signifies p < 0.01; *** signifies p < 0.001.

 


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Fig. 7. Measured soil C/N ratios on an areal basis versus C/N ratios predicted from multiple regression models in the form of Eq. [1] with {phi} = 1, using (a) vegetation factors, (b) landscape factors, and (c) landscape factors and soil chemical properties as independent variables. Regression coefficients (ai) shown for each independent variable (Ai) in order of decreasing importance to overall model. S = Southness; RS = red spruce basal area; EH = eastern hemlock basal area; StM = striped maple basal area; WA = white ash basal area (m2 ha-1); pH = mineral soil pH; * signifies p < 0.05; ** signifies p < 0.01; *** signifies p < 0.001.

 





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