Decadal Change in Soil Chemistry of Northern Hardwood Forests on the White Mountain National Forest, New Hampshire, USA

Conference Paper

Abstract

  • Core Ideas Soils in the northeastern United States show signs of recovery from acidification between 2001‐2002 and 2014. Variation in soil chemistry at the landscape and plot‐scales was similar, informing study design. Soils influenced by shallow groundwater were more dynamic than well‐drained soils. Soils are subject to a variety of stressors including human land use, air pollution and climate change. A challenge for detecting temporal change is disentangling heterogeneity at multiple spatial scales. Forty permanent plots were sampled across the US White Mountain National Forest (WMNF) in 2001 or 2002 and resampled in 2014. Paired t tests detected significant increases in carbon and base cations concentrations and a decrease in Al in the Oa horizon while base cations decreased and Al increased in some mineral horizons. A subset of six plots were intensively resampled in 2015. Pooled variances were calculated using all the six intensively sampled plots from 2014 to 2015. Within‐site variability was comparable to overall variability across the WMNF. When study sites were stratified into hydrologic groups, we found a strong signal in the Oa horizon of increasing carbon and base cation concentrations from 2001–2002 to 2014, suggesting that soils influenced by shallow groundwater contributions from upslope may be more responsive to acidification recovery than soils influenced only by vertical percolation. The initial study design did not consider the role of hydrologic pathways in susceptibility of soils to temporal change and did not include enough plots in each hydrologic group to maximize the power of this stratification approach. However, these results illustrate the potential for hydrologic stratification to improve change detection and interpretation in forest soil monitoring programs. The combined approach to hydrologic stratification and estimating variance components simultaneously at the landscape and within‐plot scales is crucial for calculating sample size needed to detect temporal change.
  • Authors

  • Fraser, Olivia L
  • Bailey, Scott W
  • Ducey, Mark
  • Status

    Publication Date

  • August 2019
  • Has Subject Area

    Digital Object Identifier (doi)

    Start Page

  • S96
  • End Page

  • S104
  • Volume

  • 83
  • Issue

  • S1