scholarly journals Mapping inshore fishing activity using aerial, land, and vessel-based sighting information

2014 ◽  
Vol 72 (2) ◽  
pp. 467-479 ◽  
Author(s):  
Patricia Breen ◽  
Koen Vanstaen ◽  
Robert W. E. Clark
Keyword(s):  
Monitoring System ◽  
Spatial Planning ◽  
Satellite Monitoring ◽  
Marine Spatial Planning ◽  
Fishing Activity ◽  
Fishing Vessels ◽  
Spatial Differences ◽  
Fisheries Interactions ◽  
Enforcement Activity ◽  
Good Agreement

Abstract Information on the distribution and intensity of inshore fishing activity is needed to inform marine spatial planning and to assess fisheries interactions with the environment and other industries. Although fishing vessels under 15 m (overall length) account for 98.4% (2011 value) by the number of the European fleet, information on inshore fishing activity in Europe is very limited as there is no statutory satellite monitoring of smaller vessels (<15 m length before 2012, <12 m thereafter). Here, we develop, present, and apply a method which uses sightings-per-unit-effort (SPUE) estimates calculated from fisheries enforcement data to describe the distribution and intensity of inshore fishing activity off the coasts of England and Wales. For the larger inshore vessels, the SPUE estimates of activity were validated with vessel monitoring system (VMS) data and showed good agreement at the scale of analysis. Fishing activity estimates from SPUE are presented with an assessment of uncertainty, to account for spatial differences in enforcement activity. Our estimates of the distribution and intensity of inshore fishing activity and will complement estimates of offshore fishing activity based on VMS.

scholarly journals Defining fishing grounds with vessel monitoring system data

2011 ◽  
Vol 69 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Simon Jennings ◽  
Janette Lee
Keyword(s):  
Environmental Management ◽  
High Resolution ◽  
Monitoring System ◽  
Unit Activity ◽  
Spatial Planning ◽  
Marine Spatial Planning ◽  
Marine Science ◽  
Fishing Activity ◽  
Planning And Management ◽  
System Data

Abstract Jennings, S., and Lee, J. 2012. Defining fishing grounds with vessel monitoring system data. – ICES Journal of Marine Science, 69: 51–63. Methods for defining fishing grounds to support marine spatial planning and management are developed, applied, and compared. The methods are broadly applicable and repeatable because they use vessel monitoring system (VMS) data that are archived and increasingly accessible. For several fleets at regional and national scales, an attempt is made to assess how the choice of criteria for defining grounds influences (i) size, shape, and location, (ii) overlap among grounds, and (iii) the extent to which annual and multi-annual patterns of fishing activity describe grounds used seasonally or by individual vessels. The results show that grounds defined by excluding infrequently fished margins (areas with <10% of total fishing activity) are typically 50% smaller than total fished area. However, landings weight or value (LWV) per unit activity can be higher at the margins, with 10% of activity usually accounting for 10–20% of LWV. The removal of fishing activity in the margins, as a consequence of regulation or fleet behaviour, would lead to disproportionately greater reductions in interactions with other fisheries, sectors, and the environment. Accessible high-resolution information on the “anatomy” of all fishing grounds would better inform debates on the allocation and the use of marine space and the integration of fisheries and environmental management.

scholarly journals A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints

2017 ◽  
Vol 75 (3) ◽  
pp. 988-998 ◽  
Author(s):  
Jennifer L Shepperson ◽  
Niels T Hintzen ◽  
Claire L Szostek ◽  
Ewen Bell ◽  
Lee G Murray ◽  
...  
Keyword(s):  
Missing Data ◽  
Monitoring System ◽  
Spline Interpolation ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Activity ◽  
Fishing Vessels ◽  
Position Data ◽  
Hermite Spline ◽  
Data Coverage

Abstract Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45–99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints.

Using AIS data from ships for MSP

2018 ◽  
Author(s):  
OCTO
Keyword(s):  
Real Time ◽  
Spatial Planning ◽  
Planning Process ◽  
Satellite Networks ◽  
International Maritime Organization ◽  
Automatic Identification ◽  
Marine Spatial Planning ◽  
Fishing Vessels

Automatic identification systems (AIS) are mandated by the International Maritime Organization to be operational on many types of ships; generally speaking, any cargo ship of 300 gross tonnes or more, and fishing vessels over 15 feet long. AIS broadcasts a ship’s identity, position, speed, and other variables as frequently as every other second, and at most every six minutes. Between VHF radio and satellite networks, ships can be monitored in real-time. Several websites offer real-time AIS data for free, making it an easy addition to a marine spatial planning process.

scholarly journals Quantifying habitat preference of bottom trawling gear

2020 ◽  
Author(s):  
N T Hintzen ◽  
G Aarts ◽  
J J Poos ◽  
K J Van der Reijden ◽  
A D Rijnsdorp
Keyword(s):  
Habitat Preference ◽  
Spatial Planning ◽  
Wind Farm ◽  
Fish Assemblages ◽  
Marine Species ◽  
Demersal Fish ◽  
Marine Spatial Planning ◽  
Nature Protection ◽  
Bottom Trawling ◽  
Fishing Vessels

Abstract Continental shelves around the world are subject to intensive bottom trawling. Demersal fish assemblages inhabiting these shelves account for one-fourth of landed wild marine species. Increasing spatial claims for nature protection and wind farm energy suppresses, however, the area available to fisheries. In this marine spatial planning discussion, it is essential to understand what defines suitable fishing grounds for bottom trawlers. We developed a statistical methodology to study the habitat preference of a fishery, accounting for spatial correlation naturally present in fisheries data using high-resolution location data of fishing vessels and environmental variables. We focused on two types of beam trawls to target sole using mechanical or electrical stimulation. Although results indicated only subtle differences in habitat preference between the two gear types, a clear difference in spatial distribution of the two gears was predicted. We argue that this change is driven by both changes in habitat preference as well as a change in target species distribution. We discuss modelling of fisheries’ habitat preference in light of marine spatial planning and as support in benthic impact assessments.

scholarly journals ScotMap: Participatory mapping of inshore fishing activity to inform marine spatial planning in Scotland

Marine Policy ◽  
2017 ◽  
Vol 79 ◽  
pp. 8-18 ◽  
Author(s):  
Andronikos Kafas ◽  
Anne McLay ◽  
Marianna Chimienti ◽  
Beth E. Scott ◽  
Ian Davies ◽  
...  
Keyword(s):  
Spatial Planning ◽  
Marine Spatial Planning ◽  
Participatory Mapping ◽  
Fishing Activity

A Feature Encoding Approach and a Cloud Computing Architecture to Map Fishing Activities

2021 ◽  
Author(s):  
A. Galdelli ◽  
A. Mancini ◽  
E. Frontoni ◽  
A. N. Tassetti
Keyword(s):  
Spatial Planning ◽  
Low Cost ◽  
Small Scale ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Spatial Planning ◽  
Fishing Activity ◽  
Feature Encoding ◽  
Encoding Method ◽  
New Policies

Abstract Monitoring fish stocks and fleets’ activities is key for Marine Spatial Planning. In recent years Vessel Monitoring System and Automatic Identification System have been developed for vessels longer than 12 and 15m in length, respectively, while small scale vessels (&lt; 12m in length) remain untracked and largely unregulated, even though they account for 83% of all fishing activity in the Mediterranean Sea. In this paper we present an architecture that makes use of a low-cost LoRa/cellular network to acquire and process positioning data from small scale vessels, and a feature encoding approach that can be easily extended to process and map small scale fisheries. The feature encoding method uses a Markov chain to model transitions between successive behavioural states (e.g., fishing, steaming) of each vessel and classify its activity. The approach is evaluated using k-fold and Leave One Boat Out cross-validations and, in both cases, it results in significant improvements in the classification of fishing activities. The use of a such low-cost and open source technology coupled to artificial intelligence could open up potential for more integrated and transparent platforms to inform coastal resource and fisheries management, and cross-border marine spatial planning. It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to the optimal use of marine resources.

scholarly journals Implications of using alternative methods of vessel monitoring system (VMS) data analysis to describe fishing activities and impacts

2012 ◽  
Vol 69 (4) ◽  
pp. 682-693 ◽  
Author(s):  
Gwladys I. Lambert ◽  
Simon Jennings ◽  
Jan Geert Hiddink ◽  
Niels T. Hintzen ◽  
Hilmar Hinz ◽  
...  
Keyword(s):  
Data Analysis ◽  
Monitoring System ◽  
Spatial Scales ◽  
Alternative Methods ◽  
Track Reconstruction ◽  
Fishing Activity ◽  
Fishing Impact ◽  
Fishing Vessels ◽  
Epifaunal Communities ◽  
Fishing Impacts

Abstract Lambert, G. I., Hiddink, J. G., Hintzen, N. T., Hinz, H., Kaiser, M. J., Murray, L. G., and Jennings, S. 2012. Implications of using alternative methods of vessel monitoring system (VMS) data analysis to describe fishing activities and impacts. – ICES Journal of Marine Science, 69: 682–693. Understanding the spatial distribution and intensity of fishing activity is a prerequisite for estimating fishing impacts on seabed biota and habitats. Vessel monitoring system data provide information on fishing activity at large spatial scales. However, successive position records can be too infrequent to describe the complex movements fishing vessels make. High-frequency position data were collected to evaluate how polling frequency and the method of analysis influenced the estimates of fishing impact on the seabed and associated epifaunal communities. Comparisons of known positions with predictions from track interpolation revealed that the performance of interpolation depended on fleet behaviour. Descriptions and indicators of fishing intensity were influenced significantly by the analytical methods (track reconstruction, density of position records) and grid-cell resolution used for the analysis. These factors can lead to an underestimation of fishing impact on epifaunal communities. It is necessary to correct for such errors to quantify the effects of fishing on various ecosystem components and hence to inform ecosystem-based management. Polling at intervals of 30 min would provide a desirable compromise between achieving precise estimates of fishing impacts on the seabed and minimizing the cost of data collection and handling.

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