JOURNAL ARTICLE

Seeing the Forest Through the Trees: New Approaches to John Bartram's Observations.

  • Published In: Transactions of the American Philosophical Society, 2026, v. 115, n. 1. P. 74 1 of 3

  • Database: Humanities Source Ultimate 2 of 3

  • Authored By: Stewart, Kacey; Fulton II, Albert E. 3 of 3

Abstract

This article focuses on a new interdisciplinary approach to John Bartram’s 1751 publication *Observations*, a journal of his 1743 botanical and diplomatic expedition through the Haudenosaunee Confederacy in central Pennsylvania and New York. Although historically criticized for its plain style and lack of narrative, the authors apply digital humanities tools and ecological statistical methods to treat Bartram’s detailed lists of flora, soil, and environmental conditions as valuable historical ecological data. Their analysis reveals Bartram’s accurate recording of species and environmental interconnections, while also highlighting his failure to recognize Indigenous land management practices—such as selective burning and silviculture—that actively shaped the forest ecosystems he described. By combining Bartram’s textual data with contemporaneous maps and paleoecological research, the study challenges the longstanding myth of “virgin wilderness” and underscores the significant ecological impact of Indigenous peoples prior to and during European contact. [Extracted from the article]

Additional Information

  • Source:Transactions of the American Philosophical Society. 2026/03, Vol. 115, Issue 1, p74
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2026
  • ISSN:00659746
  • DOI:10.1353/tap.2026.a985544
  • Accession Number:192609891
  • Copyright Statement:Copyright of Transactions of the American Philosophical Society is the property of University of Pennsylvania Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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