Considerations on neonatal ungulate capture method: potential for bias in survival estimation and cause‐specific mortality

Abstract

A recent study of Sitka black‐tailed deer Odocoileus hemionus sitkensis demonstrated that opportunistic fawn capture yielded left‐truncated data and ultimately resulted in overestimating fawn survival and spurious ecological model inference compared to neonates captured via vaginal implant transmitters (VITs). Given the ecological and economic value of ungulates worldwide and the importance of neonate survival to understanding population dynamics, the potential biases in survival estimates and causes of mortality caused by left‐truncation must be transparent. Herein, we used a VIT‐based dataset from white‐tailed deer Odocoileus virginianus to examine potential problems with left‐truncated data. We manipulated our original VIT‐based dataset by randomly assigning age‐at‐capture to create three hypothetical opportunistic samples. We used the Kaplan—Meier estimator to quantify fawn survival to 16 weeks of age for the original and hypothetical datasets. Additionally, we compared the relative importance of mortality causes between the datasets. Survival for the original, VIT‐based dataset was 0.121 (SE = 0.043), while hypothetical datasets yielded overestimates (ranging from 0.191 to 0.234). The hypothetical opportunistic samples overestimated coyote predation as a source of mortality, while underestimating starvation. Because management actions rely on accurate estimates of survival and causes of mortality, we recommend that neonatal survival studies consider biases caused by capture method. For robust estimates of survival, VIT‐based samples appear to provide better estimates of survival, as opportunistic samples are biased high. We encourage future work to elucidate the potential for neonate capture technique to affect cause‐specific mortality.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2017
Source ID
10.2981/wlb.00250

Entities

People

  • Christopher E. Moorman
  • Christopher S. Deperno
  • M. Colter Chitwood
  • Marcus A. Lashley

Organizations

  • United States Department of Defense

Tags

Fields of Study

  • Environmental science

Readers

  • Aquatic Ecology
  • Computational Modeling and Simulation
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference