Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/35207
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Enhancing data mobilisation through a centralised data repository for Atlantic salmon (Salmo salar L.): Providing the resources to promote an ecosystem-based management framework.
Author(s): 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
Contact Email: c.d.bull@stir.ac.uk
Keywords: Data mobilisation
Atlantic salmon
Salmo salar
Ecoinformatics
Metadata catalogue
Labelled property graph
Issue Date: Sep-2022
Date Deposited: 1-May-2023
Citation: Diack G, Bull C, Akenhead SA, van der Stap T, Johnson BT, Rivot E, Patin R, Hernvann P, Schubert A, Bird T, Saunders M & Crozier W (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.. <i>Ecological Informatics</i>, 70, Art. No.: 101746. https://doi.org/10.1016/j.ecoinf.2022.101746
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.
DOI Link: 10.1016/j.ecoinf.2022.101746
Rights: This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed. For commercial reuse, permission must be requested from publisher.
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

Files in This Item:
File Description SizeFormat 
1-s2.0-S1574954122001960-main.pdfFulltext - Published Version4.65 MBAdobe PDFView/Open



This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.