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Using Autonomous Recording Units for Estimating Detectability in Animal Populations
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- Author / Creator
- Yip, Daniel A
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Accurate assessment of how animals distribute themselves across the landscape is an essential component of ecological research. Ecologists often conduct surveys to subsample a representative portion of an area of interest and extrapolate their findings to a larger region. Acoustic surveys are frequently used to study a variety of organisms including birds, which rely on acoustic communication and vocalize regularly. The development of autonomous recording units (ARUs) has greatly increased the popularity of acoustic surveys, due to the ability to record surveys and leave recording equipment unattended for extended periods of time. However, variation in the detectability of wildlife is difficult to quantify, and can be strongly influenced by the focal species, the surrounding environment, and method of recording and processing data. Furthermore, count data from ARUs are typically considered to be indices of abundance, and obtaining density requires significantly more effort and data. My objectives were to investigate and quantify variables that influence detectability, determine how variation in detectability influences estimates of relative abundance, describe methods to address and correct for variation in detectability, and develop methods to efficiently estimate animal density using ARUs. I found that detectability is greatly influenced by the surrounding environment and that detectability greatly differs between open and closed vegetation types. Ignoring variation in detectability can lead to estimates of relative abundance in open vegetation types that are more than double that of closed vegetation types, due to increases in the area sampled by ARUs. Furthermore, detectability is influenced by the type of recorder used, and while sometimes equivalent to the detectability of human observers, standardization of detectability is required to properly integrate data from multiple sources. Finally, I presented methods to predict correction factors for different species and vegetation types based on the generalized sound characteristics of their vocalizations or songs. These correction factors should offset differences in detectability between different environmental conditions without the need for calibration studies. I also present a new method for estimating animal density from ARUs by estimating the distance of a vocalizing individual from the sound level measured on an audio recording. I show that these estimates are more accurate and objective than distance estimation in human observers and can provide estimates of animal density through conventional distance sampling. Integrating and standardizing data from ARUs and other sources is important when looking at large-scale ecological patterns. My research contributes to the growing number of studies that investigate variation in detectability of acoustic signals in different environments and the implication on wildlife surveys. I also provide innovative methods which should increase the versatility, quality and accuracy of data obtained from audio recordings and allow ecologists to integrate data from multiple sources to answer questions at different ecological scales.
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- Graduation date
- Spring 2020
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.