R046 - Predicting fishing effort and impact delivery plan
Updated 4 September 2020
© Crown copyright 2020
This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: psi@nationalarchives.gov.uk.
Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.
This publication is available at https://www.gov.uk/government/publications/impact-of-external-pressures-on-fisheries-in-western-waters-area-7/delivery-plan
1. Evidence requirement R046 – Predicting Fishing effort and impact
2. Requirement overview
2.1 Requirement detail
A number of MMO management decisions depend on an understanding of what fishing activity is likely to occur in the future. Currently predictions of future fishing activity are based on historic fishing catch data which does not account for environmental factors such as weather or external pressures such as quotas in other areas, stock price change, availability and diversification. Developments in data analysis and the recent expansion in the use of machine learning and artificial intelligence means improvements are possible in the way the MMO delivers its core functions. This is envisaged as a combination of existing MMO held data with additional information gathered on what other pressures may be driving fisheries behaviour.
Examples of MMO activity in the scope of this work which could benefit are:
- Under 10m vessel monthly/quarterly catch limits
- Quota trading decisions
- Scallop/crab/lobster monthly/quarterly days at sea allocation
- Marine Protected Area (MPA) assessment/management
- Marine licensing case officer decisions
- Marine compliance work
- Marine planning policy development and policy implementation
2.2 MMO use
Fisheries management: A better understanding of where fishing activity is likely to happen will allow for improved allocation of fishing vessel quota and days at sea.
Marine Conservation: A better understanding of where fishing activity is likely to happen and what the impacts will be on the natural environment will allow for improved MPA management decision making.
Planning: A better understanding of where fishing activity will happen and what the impacts are likely to be on the natural environment will allow for an improved consideration of uses of the marine space.
Compliance: A better understanding of where fishing activity is likely to happen will allow for improved risk assessment and targeting of higher risk vessels for inspection.
Blue Belt: A better understanding of where fishing activity is likely to happen in and around UK Overseas Territories will allow for improved risk assessment and deployment of patrol assets.
2.3 External interest
Natural England, Centre for Environment, Fisheries and Aquaculture Science (Cefas), Seafish
3. Aims and objectives
3.1 Aim
Improve forecasting of fisheries fleet activity, to allow better decision making.
3.2 Objectives
The objectives are as follows:
- Collate available methodologies for predicting fishing effort
- Test the most promising methodologies against real data to determine their predictive power
- Recommend which, if any, methodologies provide an effective and proportionate way of predicting fishing effort
- Identify constraints in better prediction of fishing effort
- Test the results of the fishing effort model against the MMO decision scenarios
4. Existing evidence
4.1 Academic
Soulie and Thebaud (2006) show how an agent-based modelling approach can be used to predict fisheries fleets’ response to management options, whilst Mullon et al (2016) show that, at a North East Atlantic basin level, regulation is the most important factor in determining the evolution of fisheries in the long-term. Latour et al (2011) assess four different fisheries management models that incorporate an ecosystem approach.
In recent years models have been created which use machine learning to take a large amount of data to draw detailed predictions. A recent study – Russo et al 2019, used a machine learning model, a type of Artificial Neural Network (cascaded multilayer perceptron network (CMPN)) to predict the annual fishing footprints of vessels without tracking devices (in this case under 15m) in the Adriatic Sea.
4.2 Other
Gov.uk contains a significant amount of information on current management approaches to fisheries and related information. The Cefas data hub has a catalogue of existing fisheries data that can be used in modelling.
5. Current activity
An ongoing Natural England led project with the National Federation of Fishermen’s Organisations titled Displacement of Fishing Effort from MPAs may provide useful insight for this evidence requirement. A Scallop stock assessment is being delivered through Cefas and Seafish with MMO represented on the project steering group. The MMO will monitor outputs of these projects as they emerge.
6. Associated evidence requirements
R141 - Fisheries displacement from management and other marine activities (Link TBC)
7. Further details
For more information or to add further research to the existing evidence list please email evidence@marinemanagement.org.uk
First created 26 September 2017