Graduation Year
2018
Graduation Month
May
Document Type
Thesis
Degree Name
Bachelor of Science
School or Department
Wildlife Biology
Major
Wildlife Biology – Terrestrial
Faculty Mentor Department
Wildlife Biology
Faculty Mentor
Dr. Victoria J. Dreitz
Faculty Reader(s)
Dr. Chad Bishop, Dr. Mike Mitchell
Keywords
False positive, detection, survey method, avian survey, dependent double-observer method
Subject Categories
Ornithology | Population Biology
Abstract
Imperfect detection is a known issue when conducting count-based surveys in wildlife studies. False positive detections, observed occurrences of individuals that truly are not present, are often assumed to not occur. This assumption can bias detection rates and create misleading results when calculating population estimates. Survey methods such as the dependent double-observer method are suggested to reduce the occurrence of false positives (Nichols et al. 2000). My study quantified and compared rates of false positives in a single-observer method and a dependent double-observer method using computer-generated auditory surveys. I categorized volunteer observers as either inexperienced or experienced and asked them to identify vocalizations of ten grassland songbird species native to central Montana. False positive rates of experienced observers declined from 0.095 in single-observer surveys to 0.032 in dependent double-observer surveys. False positive rates of inexperienced observers declined from 0.511 in single-observer surveys to 0.391 in dependent double-observer surveys. Further evaluation will provide information on the effectiveness of the dependent double-observer method in providing more precise and less biased population estimates.
Honors College Research Project
Yes
Recommended Citation
Strickfaden, Kaitlyn M., "Quantifying False Positives in Avian Survey Data" (2018). Undergraduate Theses, Professional Papers, and Capstone Artifacts. 201.
https://scholarworks.umt.edu/utpp/201
Included in
© Copyright 2018 Kaitlyn M. Strickfaden