Abstract Red spruce (Picea rubens Sarg.) is a commercially and ecologically important conifer species that primarily exists at northern latitudes of eastern North America and extends southward following the Appalachian Mountains into North Carolina and Tennessee. Due to a warming climate and human-caused disturbances, only fragmented, sky-island populations remain at the highest peaks of the southern Appalachians where their habitat continues to be threatened. While they have been recognized for the rare wildlife habitat they provide in the region, these populations remain understudied. This work aimed to examine differences in stem form between the northern and southern populations of red spruce and to provide additional quantitative methods for managing red spruce stands through providing regionally fitted stem taper models. First, we examined differences in stem form using two methods: a sectional rate of change in diameter and a region variable added to the Kozak (2004) Model 02 taper model. The sectional taper comparison showed significant differences (P < .05) in taper rate throughout the stem that were most pronounced below breast height and above the midpoint. The nested model comparison also showed a significant difference after performing a likelihood ratio test. These results agreed that significant differences in stem form between the two populations exist and supported the idea that localized taper models would provide the best results. Next, we evaluated four stem taper models for their ability to predict upper stem diameters and total volume in southern Appalachian red spruce: a quadratic polynomial, a segmented, a variable exponent, and a geometric model. Fitting data came from a 1992 study across three southern Appalachian spruce-fir sites. Validation data came from a new dataset collected in 2022 at Unaka Mountain, Tennessee. Based on past studies and our results, we found that the Kozak (My last words on taper equations. For Chron 2004;80:507–15. https://doi.org/10.5558/tfc80507-4.) Model 02 variable exponent and the Max and Burkhart (Segmented polynomial regression applied to taper equations. For Sci 1976;22:283–9.) segmented polynomial models performed equally well. The choice of the final model should depend on the users’ objectives and practical limitations (i.e. programming ability, availability of fitting data, error tolerance).
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