Sampling requirements and approaches to detect ecosystem shifts

Environmental monitoring is a key component of understanding and managing ecosystems. Given that most monitoring efforts are still expensive and time-consuming, it is essential that monitoring programs are designed to be efficient and effective. In many situations, the expensive part of monitoring i...

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Autores principales:	Rosalie Bruel, Easton R. White
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Time series Changepoint Monitoring Optimal sampling Cladocera Paleoecology Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/bde2092ddfa44929922817a97b098924
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spelling	oai:doaj.org-article:bde2092ddfa44929922817a97b0989242021-12-01T04:34:55ZSampling requirements and approaches to detect ecosystem shifts1470-160X10.1016/j.ecolind.2020.107096https://doaj.org/article/bde2092ddfa44929922817a97b0989242021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20310359https://doaj.org/toc/1470-160XEnvironmental monitoring is a key component of understanding and managing ecosystems. Given that most monitoring efforts are still expensive and time-consuming, it is essential that monitoring programs are designed to be efficient and effective. In many situations, the expensive part of monitoring is not sample collection, but instead sample processing, which leads to only a subset of the samples being processed. For example, sediment or ice cores can be quickly obtained in the field, but they require weeks or months of processing in a laboratory setting. Standard sub-sampling approaches often involve equally-spaced sampling on depth. We use simulations to show how many samples, and which types of sampling approaches, are the most effective in detecting ecosystem change. We test these ideas with a case study of Cladocera community assemblage indicators reconstructed from a sediment core. We demonstrate that standard approaches to sample processing are less efficient than an iterative approach. For our case study, using an optimal sampling approach would have resulted in savings of 195 person–hours—thousands of dollars in labor costs. We also show that, compared with these standard approaches, fewer samples are typically needed to achieve high statistical power. We explain how our approach can be applied to monitoring programs that rely on video records, eDNA, remote sensing, and other common tools that allow re-sampling.Rosalie BruelEaston R. WhiteElsevierarticleTime seriesChangepointMonitoringOptimal samplingCladoceraPaleoecologyEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107096- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Time series Changepoint Monitoring Optimal sampling Cladocera Paleoecology Ecology QH540-549.5
spellingShingle	Time series Changepoint Monitoring Optimal sampling Cladocera Paleoecology Ecology QH540-549.5 Rosalie Bruel Easton R. White Sampling requirements and approaches to detect ecosystem shifts
description	Environmental monitoring is a key component of understanding and managing ecosystems. Given that most monitoring efforts are still expensive and time-consuming, it is essential that monitoring programs are designed to be efficient and effective. In many situations, the expensive part of monitoring is not sample collection, but instead sample processing, which leads to only a subset of the samples being processed. For example, sediment or ice cores can be quickly obtained in the field, but they require weeks or months of processing in a laboratory setting. Standard sub-sampling approaches often involve equally-spaced sampling on depth. We use simulations to show how many samples, and which types of sampling approaches, are the most effective in detecting ecosystem change. We test these ideas with a case study of Cladocera community assemblage indicators reconstructed from a sediment core. We demonstrate that standard approaches to sample processing are less efficient than an iterative approach. For our case study, using an optimal sampling approach would have resulted in savings of 195 person–hours—thousands of dollars in labor costs. We also show that, compared with these standard approaches, fewer samples are typically needed to achieve high statistical power. We explain how our approach can be applied to monitoring programs that rely on video records, eDNA, remote sensing, and other common tools that allow re-sampling.
format	article
author	Rosalie Bruel Easton R. White
author_facet	Rosalie Bruel Easton R. White
author_sort	Rosalie Bruel
title	Sampling requirements and approaches to detect ecosystem shifts
title_short	Sampling requirements and approaches to detect ecosystem shifts
title_full	Sampling requirements and approaches to detect ecosystem shifts
title_fullStr	Sampling requirements and approaches to detect ecosystem shifts
title_full_unstemmed	Sampling requirements and approaches to detect ecosystem shifts
title_sort	sampling requirements and approaches to detect ecosystem shifts
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/bde2092ddfa44929922817a97b098924
work_keys_str_mv	AT rosaliebruel samplingrequirementsandapproachestodetectecosystemshifts AT eastonrwhite samplingrequirementsandapproachestodetectecosystemshifts
_version_	1718405866762272768

Sampling requirements and approaches to detect ecosystem shifts

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