Scavenging as an Accelerant in the Postmortem Interval Estimate: Refining Quantitative Regressions for Decomposition Analyses in Southeast Nebraska

Authors

Jenna L. Alexander, University of Nebraska-LincolnFollow

First Advisor

William R. Belcher

Date of this Version

12-2024

Document Type

Thesis

Citation

A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Arts

Major: Anthropology

Under the supervision of Professor William R. Belcher

Lincoln, Nebraska, December 2024

Comments

Copyright 2024, Jenna L. Alexander. Used by permission

Abstract

In forensic anthropology, the postmortem interval (PMI) is an estimation of the time elapsed from the moment of death to the point of discovery or examination. Contemporary approaches for estimating PMI have sought to refine broad quantitative regressions by addressing variance among intrinsic and extrinsic influences of decomposition at a regional level. Southeast Nebraska and the broader Great Plains region encompass one of the most influential agricultural ecosystems, featuring a unique climate type and a diverse spectrum of vertebrate scavengers. While there has been minimal research addressing how the biogeoclimatic conditions of Nebraska affect predicted estimates for PMI using known linear algorithms, fewer of these studies have centered on the role of scavengers as catalysts for the accelerated desiccation of soft tissue. This thesis not only emphasizes how vertebrate scavenging can significantly accelerate decomposition and skew estimations of PMI, but it also attempts to incorporate these qualitative insights into an extension factor (Ef) for Megyesi et al.’s (2005) regression formula.Two faunal substitutes were thus independently subjected to an examination observing decomposition under natural conditions for 504 hours. A cubic polynomial regression (Nebr.CD) surmising extrapolated values of accumulated degree days (ADD) in correlation with a proposed range of total body scores (TBS) for the two datasets was then aggregated with Megyesi et al.’s (2005) equation to produce an Ef of 18.93. The inclusion of the Ef yielded two distinct functions (NebrM.A and NebrM.B) for PMI estimations contingent upon an upper and lower range of TBS. Future estimates using the regressions are projected to fall within a predicted parameter of the expected ADD with a 90% reliability.

Advisor: William R. Belcher