Using Past Ecosystems to Understand Modern Climate Change: A Case Study from Utah’s House Mountain Range

Summary

This is an abstract from the "Machine-Learning Approaches to Studying Ancient Human-Environmental Interactions" session, at the 90th annual meeting of the Society for American Archaeology.

As human impacts on ecosystems accelerate, there is a growing emphasis in conservation planning towards maximizing the capacity of ecosystems to respond to anticipated changes in the near future. Doing so requires understanding how ecosystems responded to past changes (e.g., human impacts, altered climates) that occurred over timescales exceeding those of direct human observation. Paleozoological data provides such a record and documents baselines of animal communities that can be used to evaluate historic anthropogenic change and attest to the responses of species to ecosystem changes over geological timescales. This study uses paleozoological data from Utah’s House Range mountains to build a paleozoological baseline species occurrence in the area over the past 1000 years. Then, using a suite of statical analyses, including Random Forest Machine learning, we compare the paleorecord to modern zoological survey data (e.g., Arctos and VertNet) and climate records to evaluate whether anthropogenic climate change has contributed to local range shifts or extirpations, as has been predicted for the region’s montane mammals. Our results have important implications as baselines are essential to understanding how climate change has and will continue to shape ecosystems and can inform ongoing wildlife management by documenting species distributions beyond our modern observations.

This Resource is Part of the Following Collections

• Society for American Archaeology 90th Annual Meeting, Denver, CO (2025)   •
• Machine-Learning Approaches to Studying Ancient Human-Environmental Interactions

Keywords

Record Identifiers

Abstract Id(s): 52852