Abstract
Piñon-juniper is one of the most common vegetation types in the Four Corners states of the western United States (Arizona, Colorado, New Mexico, and Utah). Because of its high degree of community heterogeneity across the landscape, development of a more detailed and statistically supported classification system for piñon-juniper has been requested by regional land managers. We used a USDA Forest Service Forest Inventory and Analysis (FIA) data set from the Four Corners states to develop a statistics-based classification system for piñon-juniper vegetation. Cluster analysis was used to group piñon-juniper FIA data into community classes. Classification and regression tree analysis was then used to develop a model for predicting piñon-juniper community types. To determine which variables contributed most to classifying piñon-juniper FIA data, a random forest analysis was conducted. Results from these analyses support a six-class piñon-juniper community-type model within the Four Corners states. Using the classification tree, membership of FIA piñon-juniper communities can be accurately predicted (r2 = 0.81) using only relative overstory species abundance. Our dominance-based classification system was useful in classifying piñon-juniper community types and could be used in the field to identify broad community types and complement more refined tools available for stand-scale decisionmaking. Study Implications: Piñon-juniper vegetation communities commonly occur in the Four Corners region of the United States. We used a regional data set to develop a statistically based classification system for piñon-juniper communities. We found support for a dominance-based approach supporting initial classification into six community classes. Classes were based on different overstory species dominance patterns, stand structural characteristics (stand density index, basal area [square meters per hectare], trees per hectare, and stand age), and precipitation patterns (mean annual precipitation and monsoonal index) (Table S2). Community type can be predicted using relative overstory abundance to help managers prioritize regional areas (~6,000 acres [2,428 hectares]) for management and predict responses based on precipitation patterns, current understory tree regeneration, and plant community abundance. This system could lead to better planning documents and management decisions on a regional scale to complement more refined tools available for stand-scale management such as plant associations and detailed soil maps.