FN Archimer Export Format
PT J
TI Bringing ‘Deep Knowledge’ of Fisheries into Marine Spatial Planning
BT 
AF Said, Alicia
   Trouillet, Brice
AS 1:1;2:2;
FF 1:PDG-RBE-EM;2:;
C1 UMR-AMURE, Centre for Law and Economics of the Sea, Plouzane, France
   Université de Nantes, CNRS, UMR LETG, Nantes, France
C2 IFREMER, FRANCE
   UNIV NANTES, FRANCE
SI BREST
SE PDG-RBE-EM
UM AMURE
IN WOS Ifremer UMR
   copubli-france
   copubli-univ-france
TC 22
UR https://archimer.ifremer.fr/doc/00652/76386/77397.pdf
LA English
DT Article
DE ;Knowledge;Power;Co-production;Spatial planning;Fisheries;Data model;Technical considerations;Political scrutiny
AB In marine spatial planning (MSP), the production of knowledge about marine-based activities is fundamental because it informs the process through which policies delineating the use of space are created and maintained. This paper revises our view of knowledge—developed during the mapping and planning processes—as the undisputed factual basis on which policy is developed. Rather, it argues that the construction, management, validation, and marginalisation of different types of knowledge stemming from different stakeholders or disciplinary approaches is at the heart of policy and planning processes. Using the case of fisheries-generated knowledge in the implementation of MSP, we contend that the fisheries data informing the MSP process are still very much streamlined to classical bio-economic metrics. Such metrics fall short of describing the plural and complex knowledges that comprise fisheries, such as localised social and cultural typologies, as well as the scale and dynamics, hence, providing incomplete information for the decision-making process of MSP. In this paper, we provide a way to move towards what we conceptualize as ‘Deep Knowledge’ and propose a model that brings together of the existing datasets and integrates socio-cultural data as well as complex spatiotemporal elements, to create dynamic rather than static datasets for MSP. We furthermore argue that the process of knowledge production and the building of the parameters of such datasets, should be based on effective stakeholder participation, whose futures depend on the plans that eventually result from MSP. Finally, we recommend that the ‘Deep Knowledge’ model is adopted to inform the process of knowledge production currently being undertaken in the diverse countries engaging in the MSP process. This will result in policies that truly reflect and address the complexities that characterise fisheries, and which are legitimized through a process of knowledge co-production. 
PY 2020
PD SEP
SO Maritime Studies
SN 1872-7859
PU Springer Science and Business Media LLC
VL 19
IS 3
UT 000551023200001
BP 347
EP 357
DI 10.1007/s40152-020-00178-y
ID 76386
ER

EF