Mapping pedomemory of spodic morphology using a species distribution model

Abstract Red spruce ( Picea rubens ) ecosystems in the high elevations of the central Appalachians of the eastern United States are the focus of ongoing restoration efforts due to the valuable ecosystem services these forests provide. Recent research has shown that spodic materials still present in the soil represent the pedomemory of the historic extent of red spruce forests in the region. A dataset containing 221 points with varying spodic intensities and 29 environmental variables collected from the Monongahela National Forest in West Virginia, USA, was used to evaluate the utility of a species distribution model, Maximum Entropy (MaxEnt), for predicting the presence of spodic properties. MaxEnt was selected for evaluation because, as a presence-only model, it inherently omits absence locations and thereby reduces the risk of including false absences (i.e., herein, locations that have undergone some level of depodzolization) unlike other models previously used to predict pedomemory. Model outputs that employed three different spodic intensity class inputs—very weak to strong expression, weak to strong expression, and strong expression—resulted in similar spodic probability predictions, though there was less area mapped as transitional probabilities in the strong expression model than the two models that included weaker spodic intensity input data. Permutation importance indicated that no single or small subset of environmental variables controlled the three model outputs, perhaps because the environmental covariates may have been too coarse or not strongly enough associated with podzolization processes to be very important. When the output from the MaxEnt model using the full range of spodic intensities (very weak to strong) was compared to an output produced using a presence-absence model (random forests), there was approximately 62% agreement (where both models predicted presence or both predicted absence) for the cells in the top 40% of the predicted probabilities.