PS 46-23
Detecting differences of tissue chemistry in four northern hardwoods tree species
Nutrient budgets are commonly constructed for forest ecosystems but rarely are they monitored over time with attention to changes in nutrient concentrations. Often, nutrient accumluation rates are estimated based on changes in mass by assuming that nutrient concentrations are constant. Foliage is the tree tissue that is easiest to sample repeatedly; bark, branches and wood are rarely resampled to estimate change over time. Detecting change over time requires understanding the variability of nutrient concentrations within trees, among trees, and from year to year; any of these sources of uncertainty could lead to spurious interpretations of change over time. We investigated long-term changes of nutrient concentrations (N, P, Ca, Mg, and K) of four tissue types (foliage, bark, branch and wood) in four dominant species--American beech (Fagus grandifolia Ehrh.), yellow birch (Betula alleghaniensis Britt.), red maple (Acer rubrum L.) and sugar maple (Acer saccharamMarsh.)--between 1985-87 and 2012-13 in Huntington Forest (Newcomb, NY, USA). We also sampled trees in Hubbard Brook Experimental Forest (Woodstock, NH, USA) in 2013 to study the variability of nutrient concentrations in tissues due to different sampling positions (position of leaves in the canopy, height of bark, diameter of branch, and radius of wood).
Results/Conclusions
The variability of nutrient concentrations due to sampling position was smaller in foliage than in bark, branches, or wood. In foliage, P concentration had the smallest variation (CV < 5%) for all species. Foliar N and P also varied less than Ca, Mg, or K among replicated trees for all species. Over decades, foliar N in yellow birch increased and wood K in American beech decreased (P < 0.05). Foliar K exhibited a decreasing trend over time in all species (P < 0.05). In monitoring long-term changes in tree tissue chemistry, a lower sampling intensity is needed to detect a given rate of change in foliar N or P than other elements or tissues. Uncertainty analysis is important to designing monitoring approaches and to correctly interpreting change over time.