Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat

Research output: Contribution to journal › Journal article › Research › peer-review

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Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat. / Woodward, Guy; Morris, Olivia; Barquin, Jose; Belgrano, Andrea; Bull, Colin; de Eyto, Elvira; Friberg, Nikolai; Guobergsson, Guoni; Layer-Dobra, Katrin; Lauridsen, Rasmus B.; Lewis, Hannah M.; McGinnity, Philip; Pawar, Samraat; Rosindell, James; O'Gorman, Eoin J.

In: Frontiers in Ecology and Evolution, Vol. 9, 675261, 2021.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Woodward, G, Morris, O, Barquin, J, Belgrano, A, Bull, C, de Eyto, E, Friberg, N, Guobergsson, G, Layer-Dobra, K, Lauridsen, RB, Lewis, HM, McGinnity, P, Pawar, S, Rosindell, J & O'Gorman, EJ 2021, 'Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat', Frontiers in Ecology and Evolution, vol. 9, 675261. https://doi.org/10.3389/fevo.2021.675261

APA

Woodward, G., Morris, O., Barquin, J., Belgrano, A., Bull, C., de Eyto, E., Friberg, N., Guobergsson, G., Layer-Dobra, K., Lauridsen, R. B., Lewis, H. M., McGinnity, P., Pawar, S., Rosindell, J., & O'Gorman, E. J. (2021). Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat. Frontiers in Ecology and Evolution, 9, [675261]. https://doi.org/10.3389/fevo.2021.675261

Vancouver

Woodward G, Morris O, Barquin J, Belgrano A, Bull C, de Eyto E et al. Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat. Frontiers in Ecology and Evolution. 2021;9. 675261. https://doi.org/10.3389/fevo.2021.675261

Author

Woodward, Guy ; Morris, Olivia ; Barquin, Jose ; Belgrano, Andrea ; Bull, Colin ; de Eyto, Elvira ; Friberg, Nikolai ; Guobergsson, Guoni ; Layer-Dobra, Katrin ; Lauridsen, Rasmus B. ; Lewis, Hannah M. ; McGinnity, Philip ; Pawar, Samraat ; Rosindell, James ; O'Gorman, Eoin J. / Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat. In: Frontiers in Ecology and Evolution. 2021 ; Vol. 9.

Bibtex

@article{f28aec9b0a0f447cb40309ebf9d03c9e,
title = "Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat",
abstract = "Populations of Atlantic salmon are crashing across most of its natural range: understanding the underlying causes and predicting these collapses in time to intervene effectively are urgent ecological and socioeconomic priorities. Current management techniques rely on phenomenological analyses of demographic population time-series and thus lack a mechanistic understanding of how and why populations may be declining. New multidisciplinary approaches are thus needed to capitalize on the long-term, large-scale population data that are currently scattered across various repositories in multiple countries, as well as marshaling additional data to understand the constraints on the life cycle and how salmon operate within the wider food web. Here, we explore how we might combine data and theory to develop the mechanistic models that we need to predict and manage responses to future change. Although we focus on Atlantic salmon-given the huge data resources that already exist for this species-the general principles developed here could be applied and extended to many other species and ecosystems.",
keywords = "Atlantic salmon (Salmo salar), marine and freshwater fisheries, ecosystem-based management (EBM), matrix projection models, metabolic theory of ecology (MTE), life-stage models, size structure, SIZE STRUCTURE, ECOLOGICAL NETWORKS, FISH PRODUCTION, FRESH-WATER, BODY-SIZE, TEMPERATURE, GROWTH, SALAR, FISHERIES, ABUNDANCE",
author = "Guy Woodward and Olivia Morris and Jose Barquin and Andrea Belgrano and Colin Bull and {de Eyto}, Elvira and Nikolai Friberg and Guoni Guobergsson and Katrin Layer-Dobra and Lauridsen, {Rasmus B.} and Lewis, {Hannah M.} and Philip McGinnity and Samraat Pawar and James Rosindell and O'Gorman, {Eoin J.}",
year = "2021",
doi = "10.3389/fevo.2021.675261",
language = "English",
volume = "9",
journal = "Frontiers in Ecology and Evolution",
issn = "2296-701X",
publisher = "Frontiers Media",

}

RIS

TY - JOUR

T1 - Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat

AU - Woodward, Guy

AU - Morris, Olivia

AU - Barquin, Jose

AU - Belgrano, Andrea

AU - Bull, Colin

AU - de Eyto, Elvira

AU - Friberg, Nikolai

AU - Guobergsson, Guoni

AU - Layer-Dobra, Katrin

AU - Lauridsen, Rasmus B.

AU - Lewis, Hannah M.

AU - McGinnity, Philip

AU - Pawar, Samraat

AU - Rosindell, James

AU - O'Gorman, Eoin J.

PY - 2021

Y1 - 2021

N2 - Populations of Atlantic salmon are crashing across most of its natural range: understanding the underlying causes and predicting these collapses in time to intervene effectively are urgent ecological and socioeconomic priorities. Current management techniques rely on phenomenological analyses of demographic population time-series and thus lack a mechanistic understanding of how and why populations may be declining. New multidisciplinary approaches are thus needed to capitalize on the long-term, large-scale population data that are currently scattered across various repositories in multiple countries, as well as marshaling additional data to understand the constraints on the life cycle and how salmon operate within the wider food web. Here, we explore how we might combine data and theory to develop the mechanistic models that we need to predict and manage responses to future change. Although we focus on Atlantic salmon-given the huge data resources that already exist for this species-the general principles developed here could be applied and extended to many other species and ecosystems.

AB - Populations of Atlantic salmon are crashing across most of its natural range: understanding the underlying causes and predicting these collapses in time to intervene effectively are urgent ecological and socioeconomic priorities. Current management techniques rely on phenomenological analyses of demographic population time-series and thus lack a mechanistic understanding of how and why populations may be declining. New multidisciplinary approaches are thus needed to capitalize on the long-term, large-scale population data that are currently scattered across various repositories in multiple countries, as well as marshaling additional data to understand the constraints on the life cycle and how salmon operate within the wider food web. Here, we explore how we might combine data and theory to develop the mechanistic models that we need to predict and manage responses to future change. Although we focus on Atlantic salmon-given the huge data resources that already exist for this species-the general principles developed here could be applied and extended to many other species and ecosystems.

KW - Atlantic salmon (Salmo salar)

KW - marine and freshwater fisheries

KW - ecosystem-based management (EBM)

KW - matrix projection models

KW - metabolic theory of ecology (MTE)

KW - life-stage models

KW - size structure

KW - SIZE STRUCTURE

KW - ECOLOGICAL NETWORKS

KW - FISH PRODUCTION

KW - FRESH-WATER

KW - BODY-SIZE

KW - TEMPERATURE

KW - GROWTH

KW - SALAR

KW - FISHERIES

KW - ABUNDANCE

U2 - 10.3389/fevo.2021.675261

DO - 10.3389/fevo.2021.675261

M3 - Journal article

VL - 9

JO - Frontiers in Ecology and Evolution

JF - Frontiers in Ecology and Evolution

SN - 2296-701X

M1 - 675261

ER -

ID: 345641338