Diet uncertainty analysis strenthens model-derived indicators of food web structure and function

Research output: Contribution to journalArticle

Standard

Diet uncertainty analysis strenthens model-derived indicators of food web structure and function. / Bentley, Jacob W; Hines, David; Borrett, Stuart; Serpetti, Natalia; Fox, Clive; Reid, Dave; Heymans, Johanna J.

In: Ecological Indicators, Vol. 98, 06.03.2019, p. 239-250.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Bentley, Jacob W ; Hines, David ; Borrett, Stuart ; Serpetti, Natalia ; Fox, Clive ; Reid, Dave ; Heymans, Johanna J. / Diet uncertainty analysis strenthens model-derived indicators of food web structure and function. In: Ecological Indicators. 2019 ; Vol. 98. pp. 239-250

Bibtex

@article{0bf50b07efb44ff8882635c51f1bf651,
title = "Diet uncertainty analysis strenthens model-derived indicators of food web structure and function",
abstract = "Ecological Network Analysis (ENA) can inform marine management decisions by producing indicators that describe ecosystem health and function. Reporting ENA indicators with uncertainty boundaries lets end-users draw stronger inferences and can increase confidence in model results. However, few studies developing these indicators have estimated uncertainty due to data limitations and computational challenges. In this study, we used Linear Inverse Modelling with an Ecopath model of the Irish Sea to investigate how the incorporation of uncertainty in dietary data can strengthen inferences based on model-derived ENA indicators. A Monte Carlo approach was used to generate ten thousand data-bound parameterisations for the Irish Sea food web and provide plausibledistribution estimates for functional group diets. ENA results captured the plausible range of state-indicators and provided robust estimates of the control exerted by components within the food web. Results suggest that, highertrophic components, such as mammals, birds, and elasmobranchs in the Irish Sea are controlled by mid-to-low trophic components, such as small pelagic fish, invertebrates, and plankton. Fisheries discards also played an importantrole in the flow of energy to groups such as Nephrops (Norway lobster), crabs and lobsters, and seabirds. These results bolster our understanding of food web dynamics in the Irish Sea and demonstrate how information derived from ENA indicators can have implications for effective and sustainable ecosystem based management. Finally, the methods established here represent an important step in the maturation of marine ecosystem modelling and ENA for management purposes.",
keywords = "Food web , Ecopath, Ecological Network Analysis, Linear inverse modelling, Ecosystem based management",
author = "Bentley, {Jacob W} and David Hines and Stuart Borrett and Natalia Serpetti and Clive Fox and Dave Reid and Heymans, {Johanna J}",
note = "\{circledC} 2018 Elsevier Ltd. All rights reserved.",
year = "2019",
month = "3",
day = "6",
doi = "10.1016/j.ecolind.2018.11.008",
language = "English",
volume = "98",
pages = "239--250",
journal = "Ecological Indicators",
issn = "1470-160X",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Diet uncertainty analysis strenthens model-derived indicators of food web structure and function

AU - Bentley,Jacob W

AU - Hines,David

AU - Borrett,Stuart

AU - Serpetti,Natalia

AU - Fox,Clive

AU - Reid,Dave

AU - Heymans,Johanna J

N1 - © 2018 Elsevier Ltd. All rights reserved.

PY - 2019/3/6

Y1 - 2019/3/6

N2 - Ecological Network Analysis (ENA) can inform marine management decisions by producing indicators that describe ecosystem health and function. Reporting ENA indicators with uncertainty boundaries lets end-users draw stronger inferences and can increase confidence in model results. However, few studies developing these indicators have estimated uncertainty due to data limitations and computational challenges. In this study, we used Linear Inverse Modelling with an Ecopath model of the Irish Sea to investigate how the incorporation of uncertainty in dietary data can strengthen inferences based on model-derived ENA indicators. A Monte Carlo approach was used to generate ten thousand data-bound parameterisations for the Irish Sea food web and provide plausibledistribution estimates for functional group diets. ENA results captured the plausible range of state-indicators and provided robust estimates of the control exerted by components within the food web. Results suggest that, highertrophic components, such as mammals, birds, and elasmobranchs in the Irish Sea are controlled by mid-to-low trophic components, such as small pelagic fish, invertebrates, and plankton. Fisheries discards also played an importantrole in the flow of energy to groups such as Nephrops (Norway lobster), crabs and lobsters, and seabirds. These results bolster our understanding of food web dynamics in the Irish Sea and demonstrate how information derived from ENA indicators can have implications for effective and sustainable ecosystem based management. Finally, the methods established here represent an important step in the maturation of marine ecosystem modelling and ENA for management purposes.

AB - Ecological Network Analysis (ENA) can inform marine management decisions by producing indicators that describe ecosystem health and function. Reporting ENA indicators with uncertainty boundaries lets end-users draw stronger inferences and can increase confidence in model results. However, few studies developing these indicators have estimated uncertainty due to data limitations and computational challenges. In this study, we used Linear Inverse Modelling with an Ecopath model of the Irish Sea to investigate how the incorporation of uncertainty in dietary data can strengthen inferences based on model-derived ENA indicators. A Monte Carlo approach was used to generate ten thousand data-bound parameterisations for the Irish Sea food web and provide plausibledistribution estimates for functional group diets. ENA results captured the plausible range of state-indicators and provided robust estimates of the control exerted by components within the food web. Results suggest that, highertrophic components, such as mammals, birds, and elasmobranchs in the Irish Sea are controlled by mid-to-low trophic components, such as small pelagic fish, invertebrates, and plankton. Fisheries discards also played an importantrole in the flow of energy to groups such as Nephrops (Norway lobster), crabs and lobsters, and seabirds. These results bolster our understanding of food web dynamics in the Irish Sea and demonstrate how information derived from ENA indicators can have implications for effective and sustainable ecosystem based management. Finally, the methods established here represent an important step in the maturation of marine ecosystem modelling and ENA for management purposes.

KW - Food web

KW - Ecopath

KW - Ecological Network Analysis

KW - Linear inverse modelling

KW - Ecosystem based management

U2 - 10.1016/j.ecolind.2018.11.008

DO - 10.1016/j.ecolind.2018.11.008

M3 - Article

VL - 98

SP - 239

EP - 250

JO - Ecological Indicators

T2 - Ecological Indicators

JF - Ecological Indicators

SN - 1470-160X

ER -

ID: 3374294