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Some Like It Cool : = A Study of Odontocete Ecology in the Hawaiian Islands Using Passive Acoustic Monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Some Like It Cool :/
其他題名:	A Study of Odontocete Ecology in the Hawaiian Islands Using Passive Acoustic Monitoring.
作者:	Ziegenhorn, Morgan Adair.
面頁冊數:	1 online resource (166 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Biological oceanography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29320336click for full text (PQDT)
ISBN:	9798351430706

Some Like It Cool : = A Study of Odontocete Ecology in the Hawaiian Islands Using Passive Acoustic Monitoring.
Ziegenhorn, Morgan Adair.

Some Like It Cool :A Study of Odontocete Ecology in the Hawaiian Islands Using Passive Acoustic Monitoring. - 1 online resource (166 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2022.

Includes bibliographical references

Studies of marine mammals using passive acoustic monitoring (PAM) tools are becoming more and more common. This methodology allows for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. In the Hawaiian Islands, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected from 2008-2019 at sites off the islands of Hawaiʻi, Kauaʻi, and Pearl and Hermes Reef (otherwise known as 'Manawai'). However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. In this dissertation, a machine learning toolkit was used to effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Classified clicks were distilled into timeseries of species' presence in order to document, and propose reasons for, observed patterns. Habitat modelling employing Generalized Additive Models (GAMs) with and without Generalized Estimating Equations (GEEs) was used to elucidate these trends in combination with oceanographic variables. The machine learning pipeline used distilled eight unique echolocation click types, attributable to eight or more species of odontocetes. Species composition differed amongst considered sites, and this difference was robust to seasonal movement patterns. Temporally, hour of day was the most significant predictor of detection across species and sites, followed by season. When considered in conjunction with sea surface variables, temperature had the strongest relationship to detections. Of the climate indices considered, El Nino Southern Oscillation (ENSO) may have the most effect on species detections at monitored sites. This study demonstrates that PAM is an invaluable tool in studies of oceanic top predators, and that machine learning tools can mitigate issues related to the size and complexity of PAM datasets. Using these tools and habitat modelling analyses, we can gain valuable insights into top predator behavior in relation to temporal variables, surface conditions, and long-term climate indicators.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351430706Subjects--Topical Terms:

2122748
Biological oceanography.
Subjects--Index Terms:

Passive acoustic monitoringIndex Terms--Genre/Form:

542853
Electronic books.

Some Like It Cool : = A Study of Odontocete Ecology in the Hawaiian Islands Using Passive Acoustic Monitoring.
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520 $a Studies of marine mammals using passive acoustic monitoring (PAM) tools are becoming more and more common. This methodology allows for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. In the Hawaiian Islands, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected from 2008-2019 at sites off the islands of Hawaiʻi, Kauaʻi, and Pearl and Hermes Reef (otherwise known as 'Manawai'). However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. In this dissertation, a machine learning toolkit was used to effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Classified clicks were distilled into timeseries of species' presence in order to document, and propose reasons for, observed patterns. Habitat modelling employing Generalized Additive Models (GAMs) with and without Generalized Estimating Equations (GEEs) was used to elucidate these trends in combination with oceanographic variables. The machine learning pipeline used distilled eight unique echolocation click types, attributable to eight or more species of odontocetes. Species composition differed amongst considered sites, and this difference was robust to seasonal movement patterns. Temporally, hour of day was the most significant predictor of detection across species and sites, followed by season. When considered in conjunction with sea surface variables, temperature had the strongest relationship to detections. Of the climate indices considered, El Nino Southern Oscillation (ENSO) may have the most effect on species detections at monitored sites. This study demonstrates that PAM is an invaluable tool in studies of oceanic top predators, and that machine learning tools can mitigate issues related to the size and complexity of PAM datasets. Using these tools and habitat modelling analyses, we can gain valuable insights into top predator behavior in relation to temporal variables, surface conditions, and long-term climate indicators.
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