Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conduc...

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Autores principales:	Justin Kitzes, Rachael Blake, Sara Bombaci, Melissa Chapman, Sandra M. Duran, Tao Huang, Maxwell B. Joseph, Samuel Lapp, Sergio Marconi, William K. Oestreich, Tessa A. Rhinehart, Anna K. Schweiger, Yiluan Song, Thilina Surasinghe, Di Yang, Kelsey Yule
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	biogeography deep learning macroecology monitoring neural network sensor Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/67ef1bd5ba264017b3883cfc53d79913
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spelling	oai:doaj.org-article:67ef1bd5ba264017b3883cfc53d799132021-11-29T07:06:42ZExpanding NEON biodiversity surveys with new instrumentation and machine learning approaches2150-892510.1002/ecs2.3795https://doaj.org/article/67ef1bd5ba264017b3883cfc53d799132021-11-01T00:00:00Zhttps://doi.org/10.1002/ecs2.3795https://doaj.org/toc/2150-8925Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.Justin KitzesRachael BlakeSara BombaciMelissa ChapmanSandra M. DuranTao HuangMaxwell B. JosephSamuel LappSergio MarconiWilliam K. OestreichTessa A. RhinehartAnna K. SchweigerYiluan SongThilina SurasingheDi YangKelsey YuleWileyarticlebiogeographydeep learningmacroecologymonitoringneural networksensorEcologyQH540-549.5ENEcosphere, Vol 12, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	biogeography deep learning macroecology monitoring neural network sensor Ecology QH540-549.5
spellingShingle	biogeography deep learning macroecology monitoring neural network sensor Ecology QH540-549.5 Justin Kitzes Rachael Blake Sara Bombaci Melissa Chapman Sandra M. Duran Tao Huang Maxwell B. Joseph Samuel Lapp Sergio Marconi William K. Oestreich Tessa A. Rhinehart Anna K. Schweiger Yiluan Song Thilina Surasinghe Di Yang Kelsey Yule Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
description	Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.
format	article
author	Justin Kitzes Rachael Blake Sara Bombaci Melissa Chapman Sandra M. Duran Tao Huang Maxwell B. Joseph Samuel Lapp Sergio Marconi William K. Oestreich Tessa A. Rhinehart Anna K. Schweiger Yiluan Song Thilina Surasinghe Di Yang Kelsey Yule
author_facet	Justin Kitzes Rachael Blake Sara Bombaci Melissa Chapman Sandra M. Duran Tao Huang Maxwell B. Joseph Samuel Lapp Sergio Marconi William K. Oestreich Tessa A. Rhinehart Anna K. Schweiger Yiluan Song Thilina Surasinghe Di Yang Kelsey Yule
author_sort	Justin Kitzes
title	Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
title_short	Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
title_full	Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
title_fullStr	Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
title_full_unstemmed	Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
title_sort	expanding neon biodiversity surveys with new instrumentation and machine learning approaches
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/67ef1bd5ba264017b3883cfc53d79913
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Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

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