Enhancing data mobilisation through a centralised data repository for Atlantic salmon (Salmo salar L.): Providing the resources to promote an ecosystem-based management framework.

Type Article
Date 2022-09
Language English
Author(s) Diack Graeme1, Bull Colin1, 2, Akenhead Scott A.3, Van Der Stap Tim4, Johnson Brett T.4, Rivot Etienne5, Patin Rémi5, Hernvann Pierre-Yves5, 6, Schubert Aidan7, Bird Tom8, Saunders Mark7, Crozier Walter1
Affiliation(s) 1 : Atlantic Salmon Trust, Battleby House, Redgorton, Perth PH1 3EW, UK
2 : Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK
3 : Independent. 11810 Fairtide Road, North Oyster, BC V9G1K5, Canada
4 : Hakai Institute, PO Box 25039, Campbell River, BC V9W 0B7, Canada
5 : DECOD (Ecosystem Dynamics and Sustainability), Institut Agro, INRAE, IFREMER, 35042 Rennes, France
6 : Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, California, USA
7 : North Pacific Anadromous Fish Commission, Suite 502, 889 West Pender Street, Vancouver, B.C. V6C 3B2, Canada
8 : Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Rd, Nanaimo, BC V9T 6N7, Canada
Source Ecological Informatics (1574-9541) (Elsevier BV), 2022-09 , Vol. 70 , P. 101746 (9p.)
DOI 10.1016/j.ecoinf.2022.101746
WOS© Times Cited 3
Keyword(s) Data mobilisation, Atlantic salmon, Salmo salar, Ecoinformatics, Metadata catalogue, Labelled property graph
Abstract

Data and knowledge mobilisation are significant challenges in ecology and resource management, with the journey from data collection through to management action often left incomplete due to difficulties sharing information across diverse and dispersed communities. This disconnect between science and management must be resolved if we are to successfully tackle the increasing impact of human activity on our ecosystems. Across their North Atlantic range, Atlantic salmon (Salmo salar L.) populations are in steep decline in many areas and urgent actions are required to curb this decline. Being commercially important this species has been subject to intense research, but management action often suffers from both a lack of access to this knowledge resource and support for its integration into effective management strategies. To respond to this challenge, the science and management communities must place higher priority on mobilising existing and emerging knowledge sources to inform current and future resource use and mitigation strategies. This approach requires a more complete picture of the current salmon ecology data and knowledge landscape, new mechanisms to enable data mobilisation and re-use, and new research to describe and parameterise the responses of wild populations to habitat changes. Here we present a unique interface for registering and linking data resources relevant to the Atlantic salmon life cycle that can address the data mobilisation aspect of these challenges. The Salmon Ecosystem Data Hub is a salmon-specific metadata catalogue, natively interoperable with many existing data portals, which creates a low resistance pathway to maximise visibility of data relevant to Atlantic salmon. This includes the capacity to annotate datasets with life-stage domains and variable classes, thereby permitting dispersed data to be formally contextualised and integrated to support hypotheses specific to scenario-based modelling and decision-making. The alignment and mobilisation of data within the Salmon Ecosystem Data Hub will help advance the development of appropriate environmentally driven forecast models and an ecosystem-based management approach for Atlantic salmon that optimises future management strategies.

 

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Diack Graeme, Bull Colin, Akenhead Scott A., Van Der Stap Tim, Johnson Brett T., Rivot Etienne, Patin Rémi, Hernvann Pierre-Yves, Schubert Aidan, Bird Tom, Saunders Mark, Crozier Walter (2022). Enhancing data mobilisation through a centralised data repository for Atlantic salmon (Salmo salar L.): Providing the resources to promote an ecosystem-based management framework. Ecological Informatics, 70, 101746 (9p.). Publisher's official version : https://doi.org/10.1016/j.ecoinf.2022.101746 , Open Access version : https://archimer.ifremer.fr/doc/00786/89844/