Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon-A Keystone Species Under Threat
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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.
I: Frontiers in Ecology and Evolution, Bind 9, 675261, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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