Graduation Year

2026

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

Victoria Dreitz

Faculty Reader(s)

Erim Gomez, Michael Musick

Keywords

False-positive, detection-based surveys, immersive sound environment, calibration method

Subject Categories

Ornithology | Population Biology

Abstract

Detection-based surveys are widely conducted to monitor wildlife populations and provide data for estimating density and abundance. Avian count surveys are especially common because birds respond quickly to habitat degradation and restoration, making them effective indicator species. However, these detection-based surveys rely heavily on human observers and are prone to errors. If left unaddressed, these errors can bias estimates of species occurrence, and abundance, leading to misleading ecological inferences and management decisions. False-positive errors have received limited attention, in part because they are difficult to identify without independent confirmation of true species. To investigate false-positive rates in a controlled setting, we used a calibration model involving ten grassland bird species. Under this framework, species presence is known with certainty, allowing observer detections to be directly compared to control data to accurately estimate false-positive rates. We investigated two mechanisms that generate false-positive detections: 1) the number of bird species singing simultaneously and 2) the direction from which bird songs were emitted in relation to observer position. Results show that false-positive rates decrease for every additional bird, while false-negative rates increase. This reflects a potential trade-off between the two types of human errors. Additionally, false-positive rates were higher when bird songs were played behind the observer. These findings contribute to the development of empirically grounded human-error frameworks that can be applied to analytical models to adjust population estimates according to the extent of error caused by varying factors.

GLI Capstone Project

no

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© Copyright 2026 Katia Elena Chavez