A Management-Oriented Classification of Pinyon-Juniper Woodlands of the Great Basin

Abstract

Pinyon-juniper woodlands occupy about 18 percent (7.1 million ha, 17.6 million acres) of the land area of the Great Basin (Tueller and others 1979). The associated tree species are found over a wide range of environmental conditions extending from communities representative of the upper fringes of the Mohave Desert to communities found at the lower fringes of high mountain forests. Over this spatial and elevational range, communities associated with pinyon-juniper woodlands are highly variable, with complex distribution and compositional patterns. This variability is due to climatic changes occurring over the last 10,000 years and to variation in current environmental conditions (Nowak and others 1994a; Tausch and others 1993). While juniper has been present somewhere in the area for over 30,000 years (Nowak and others 1994a,b), pinyon is a relatively recent addition with a presence ranging from less than 2,000 to about 8,000 years depending on location. Over the last century many changes have occurred in these woodlands and both the types and the pace of change could potentially increase into the future. In order to successfully inventory, plan, manage, and monitor complex wildlands like the pinyon-juniper woodlands, ecological classification is required. Ecological classifications result in several benefits. The resulting hierarchy of strata can provide guidelines for the collection and retrieval of both factual and interpretive information. Results and experiences from particular sites can be compared to other unstudied sites that are shown to be relatively similar by classification. This can increase the chances of the repetition of successful management actions and reduce the chances of failure. Research, particularly that research attempting to refine interpretations of actual data, can also be better focused if sites are related to an existing classification scheme. Creation of a hierarchy of ecological strata of increasing similarity enhances interpretation through both extrapolation and interpolation of survey data, research results, and management experiences

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