Frontiers in Earth Science (Jun 2020)

Ecosystem Engineering Among Ancient Pastoralists in Northern Central Asia

  • Alicia R. Ventresca Miller,
  • Alicia R. Ventresca Miller,
  • Robert Spengler,
  • Ashleigh Haruda,
  • Bryan Miller,
  • Bryan Miller,
  • Shevan Wilkin,
  • Sarah Robinson,
  • Patrick Roberts,
  • Patrick Roberts,
  • Nicole Boivin,
  • Nicole Boivin,
  • Nicole Boivin,
  • Nicole Boivin

DOI
https://doi.org/10.3389/feart.2020.00168
Journal volume & issue
Vol. 8

Abstract

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Ecosystem engineering is an innovative concept that recognizes that organisms impact their environment, and that these changes can be detected over time. Thus, additional datasets from the ecological longue durée are necessary, specifically in response to the onset of the Anthropocene and the impacts of humans and their commensal organisms upon ecologies of all scales. For example, the management and herding of domesticated animals are recognized as having dramatic implications for soil stability, vegetation coverage, and even atmospheric composition the world over. Yet, the point at which pastoralism became a recognizable factor in altering earth systems, with large-scale environmental ramifications, is poorly understood. Here, we respond to this by reviewing and presenting data from the archeological and paleoenvironmental record across northern Central Asia in order to assess broader ecosystem impacts of pastoralism, from time periods when this economic pattern was a relatively novel component of local ecologies and involved limited population densities, through to periods in which it became intensive, coincident with agriculture, and linked to increased sedentism. Probing diverse, published analytical datasets and case studies, we examine pastoral adaptations and environmental impacts, highlighting a region where tensions surrounding resilience and sustainability of pastoralism have peaked in modern times. We draw upon these findings to examine the challenges faced by pastoralists today, and the ways in which archeological data might inform on management decisions into the future.

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