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.
Original language | English (US) |
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Pages (from-to) | 687-699 |
Number of pages | 13 |
Journal | Forest Science |
Volume | 66 |
Issue number | 6 |
DOIs | |
State | Published - Dec 1 2020 |
Keywords
- Forest inventory
- Monsoon
- Random forests
- Vegetation classification
ASJC Scopus subject areas
- Forestry
- Ecology
- Ecological Modeling