Contextualizing Estimated Tree Densities and Expert-Classified Ecosystems in the Historical Midwestern United States, a Region with Exposure to Frequent Fires
2025
Brice B. Hanberry | Charles M. Ruffner | Robert Tatina
Many ecosystems have been altered since European colonization, resulting in the loss of historical ecosystems along with information about historical ecosystems. Tree density estimation from historical land surveys with alignment to expert classifications of historical vegetation strengthen reconstructions of vegetation history through research triangulation. For the midwestern United States, we extended historical tree density estimates (&ge:12.7 cm in diameter) to contextualize expert classifications of vegetation types in Illinois and Minnesota, part of the historical Great Plains grasslands with very frequent fire exposure, and Indiana and southern Michigan, which were more protected from fire. We also identified a tree density threshold between grasslands and savannas and contrasted density estimates with two alternate density estimates. After refining expert-classified vegetation types, out of 14 major historical ecosystems in this region, 11 were grasslands, savannas, or woodlands. The three additional ecosystems were American beech (Fagus grandifolia) closed woodlands and forests in Indiana and American beech-oak (Quercus) closed woodlands and forests and tamarack (Larix laricina) and ash (Fraxinus) swamp forests in southern Michigan. Because tree densities in the grasslands of Illinois and Minnesota did not exceed 4 trees/ha and tree densities in the savannas of Indiana, Michigan, and Minnesota ranged from 23 trees/ha to 78 trees/ha, around 15 trees/ha may be a reasonable threshold between grasslands and savannas. Density estimates generally matched with two other sources of density estimates, despite using different approaches, supporting the reliability of density estimation. Anchoring density estimates from land surveys to other sources of historical vegetation establishes the validity of density estimation, while supplementing expert-classified ecosystems.
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