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A predictive model to locate ancient forests in the Cross Timbers of Osage County, Oklahoma
Authors:Matthew Therrell,&   David Stahle
Affiliation:Tree-Ring Laboratory, University of Arkansas, Ozark Hall 108A, Fayetteville, AR 72701, U.S.A.
Abstract:The Cross Timbers are a mosaic of upland deciduous forest, savanna, and glade that typifies the broad ecotone between the eastern deciduous forest and the grasslands of the southern Great Plains. The pre-settlement Cross Timbers may have covered some 7,909,700 ha from central Texas, across Oklahama into eastern Kansas, and today may represent the least disturbed forest ecosystem of comparable size still left in the eastern United States. Extensive tree-ring research indicates that ancient forests dominated by 200 to 400 year old post-oaks ( Quercus stellata Wang) survive throughout the Cross Timbers, particularly in Oklahoma. These ancient forests persist largely because the Cross Timbers formation is non-commercial for timber production, and has not experienced large-scale industrial logging. Because ancient forest relics are often found on stressful non-commercial sites in the Cross Timbers and elsewhere, it is possible to design predictive models to locate the specific terrain where undisturbed forests are likely to survive. A predictive model for southern Osage County, Oklahoma, was developed based on the steep, infertile soils of the Niotaze-Darnell complex. We tested the model with field inspection and tree-ring analysis of fifty randomly selected belt transects, and 74% of the sampled terrain is still old-growth Cross Timbers woodland. This translates into 8200 ha of ancient Cross Timbers on this single site type in southern Osage County. The abundance of ancient forest in the Cross Timbers is not widely appreciated. However, large contiguous tracts of ancient Cross Timbers up to 700 ha were identified with this predictive model, strongly supporting inferences concerning the relatively undisturbed nature of this ecosystem.
Keywords:Ancient forests    cross timbers    dendrochronology    old growth    predictive model
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