Cetacean distribution models based on visual and passive acoustic data

Abstract Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board sur...

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Autores principales:	Kaitlin E. Frasier, Lance P. Garrison, Melissa S. Soldevilla, Sean M. Wiggins, John A. Hildebrand
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f8d34cef314d4e9ea25cce8ff4539666
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spelling	oai:doaj.org-article:f8d34cef314d4e9ea25cce8ff45396662021-12-02T14:26:15ZCetacean distribution models based on visual and passive acoustic data10.1038/s41598-021-87577-12045-2322https://doaj.org/article/f8d34cef314d4e9ea25cce8ff45396662021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87577-1https://doaj.org/toc/2045-2322Abstract Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier’s beaked whales, sperm whales, and Risso’s dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.Kaitlin E. FrasierLance P. GarrisonMelissa S. SoldevillaSean M. WigginsJohn A. HildebrandNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kaitlin E. Frasier Lance P. Garrison Melissa S. Soldevilla Sean M. Wiggins John A. Hildebrand Cetacean distribution models based on visual and passive acoustic data
description	Abstract Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier’s beaked whales, sperm whales, and Risso’s dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.
format	article
author	Kaitlin E. Frasier Lance P. Garrison Melissa S. Soldevilla Sean M. Wiggins John A. Hildebrand
author_facet	Kaitlin E. Frasier Lance P. Garrison Melissa S. Soldevilla Sean M. Wiggins John A. Hildebrand
author_sort	Kaitlin E. Frasier
title	Cetacean distribution models based on visual and passive acoustic data
title_short	Cetacean distribution models based on visual and passive acoustic data
title_full	Cetacean distribution models based on visual and passive acoustic data
title_fullStr	Cetacean distribution models based on visual and passive acoustic data
title_full_unstemmed	Cetacean distribution models based on visual and passive acoustic data
title_sort	cetacean distribution models based on visual and passive acoustic data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f8d34cef314d4e9ea25cce8ff4539666
work_keys_str_mv	AT kaitlinefrasier cetaceandistributionmodelsbasedonvisualandpassiveacousticdata AT lancepgarrison cetaceandistributionmodelsbasedonvisualandpassiveacousticdata AT melissassoldevilla cetaceandistributionmodelsbasedonvisualandpassiveacousticdata AT seanmwiggins cetaceandistributionmodelsbasedonvisualandpassiveacousticdata AT johnahildebrand cetaceandistributionmodelsbasedonvisualandpassiveacousticdata
_version_	1718391405915668480

Cetacean distribution models based on visual and passive acoustic data

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