Using Vessel Monitoring System Data to Identify and Characterize Trips Made by Fishing Vessels in the United States North Pacific

Time spent fishing is the effort metric often studied in fisheries but it may under-represent the effort actually expended by fishers. Entire fishing trips, from the time vessels leave port until they return, may prove more useful for examining trends in fleet dynamics, fisher behavior, and fishing costs. However, such trip information is often difficult to resolve. We identified ~30,000 trips made by vessels that targeted walleye pollock (Gadus chalcogrammus) in the Eastern Bering Sea from 2008–2014 by using vessel monitoring system (VMS) and landings data. We compared estimated trip durations to observer data, which were available for approximately half of trips. Total days at sea were estimated with < 1.5% error and 96.4% of trip durations were either estimated with < 5% error or they were within expected measurement error. With 99% accuracy, we classified trips as fishing for pollock, for another target species, or not fishing. This accuracy lends strong support to the use of our method with unobserved trips across North Pacific fisheries. With individual trips resolved, we examined potential errors in datasets which are often viewed as “the truth.” Despite having > 5 million VMS records (timestamps and vessel locations), this study was as much about understanding and managing data errors as it was about characterizing trips. Missing VMS records were pervasive and they strongly influenced our approach. To understand implications of missing data on inference, we simulated removal of VMS records from trips. Removal of records straightened (i.e., shortened) vessel trajectories, and travel distances were underestimated, on average, by 1.5–13.4% per trip. Despite this bias, VMS proved robust for trip characterization and for improved quality control of human-recorded data. Our scrutiny of human-reported and VMS data advanced our understanding of the potential utility and challenges facing VMS users globally.

[1]  R. Lauth,et al.  Detecting temporal trends and environmentally-driven changes in the spatial distribution of bottom fishes and crabs on the eastern Bering Sea shelf , 2013 .

[2]  A. Haynie,et al.  Climatic and economic drivers of the Bering Sea walleye pollock (Theragra chalcogramma) fishery: implications for the future , 2013 .

[3]  Susan E. Wigley,et al.  Using Positional Data from Vessel Monitoring Systems to Validate the Logbook-Reported Area Fished and the Stock Allocation of Commercial Fisheries Landings , 2009 .

[4]  Gerjan J. Piet,et al.  Improved estimation of trawling tracks using cubic Hermite spline interpolation of position registration data , 2010 .

[5]  Ronan Fablet,et al.  Hidden Markov Models: The Best Models for Forager Movements? , 2013, PloS one.

[6]  Michael Collins Palmer.  Calculation of distance traveled by fishing vessels using GPS positional data: A theoretical evaluation of the sources of error , 2008 .

[7]  S. Kasperski,et al.  Income diversification and risk for fishermen , 2013, Proceedings of the National Academy of Sciences.

[8]  Claire M. Postlethwaite,et al.  Effects of Temporal Resolution on an Inferential Model of Animal Movement , 2013, PloS one.

[9]  Martin Renner,et al.  A spatial-seasonal analysis of the oiling risk from shipping traffic to seabirds in the Aleutian Archipelago. , 2015, Marine pollution bulletin.

[10]  Stefano Cataudella,et al.  VMSbase: An R-Package for VMS and Logbook Data Management and Analysis in Fisheries Ecology , 2014, PloS one.

[11]  Gerjan J. Piet,et al.  VMStools: Open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data , 2012 .

[12]  Simon Jennings,et al.  Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data , 2007 .

[13]  Lars Linsen,et al.  Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction , 2014 .

[14]  Catherine M. Dichmont,et al.  Can vessel monitoring system data also be used to study trawling intensity and population depletion? The example of Australia's northern prawn fishery , 2005 .

[15]  Ronan Fablet,et al.  Ecosystem scenarios shape fishermen spatial behavior. The case of the Peruvian anchovy fishery in the Northern Humboldt Current System , 2014 .

[16]  Simon Jennings,et al.  Defining fishing grounds with vessel monitoring system data , 2012 .

[17]  Nicholas K. Dulvy,et al.  Fuelling the decline in UK fishing communities , 2010 .

[18]  Paul Marchal,et al.  A comparative analysis of métiers and catch profiles for some French demersal and pelagic fleets , 2008 .

[19]  Simon Jennings,et al.  Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data , 2010 .

[20]  A. Haynie,et al.  An expected profit model for monetizing fishing location choices , 2010 .

[21]  ChangShui-Kai,et al.  Deriving high-resolution spatiotemporal fishing effort of large-scale longline fishery from vessel monitoring system (VMS) data and validated by observer data , 2014 .

[22]  Michael J. Fogarty,et al.  Effort distribution and catch patterns adjacent to temperate MPAs , 2005 .

[23]  Jason T. Holt,et al.  Projected impacts of climate change on marine fish and fisheries , 2013 .

[24]  K. Drinkwater,et al.  The response of Atlantic cod (Gadus morhua) to future climate change , 2005 .

[25]  K. Criddle,et al.  Dysfunction by design: Consequences of limitations on transferability of catch shares in the Alaska pollock fishery , 2013 .

[26]  Jason S. Link,et al.  Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf , 2009 .

[27]  V. Stelzenmüller,et al.  Spatio-temporal patterns of fishing pressure on UK marine landscapes, and their implications for spatial planning and management , 2008 .

[28]  Clara Ulrich,et al.  Detailed mapping of fishing effort and landings by coupling fishing logbooks with satellite-recorded vessel geo-location , 2010 .

[29]  Alan Y. Chiang,et al.  Generalized Additive Models: An Introduction With R , 2007, Technometrics.

[30]  Tommaso Russo,et al.  New insights in interpolating fishing tracks from VMS data for different métiers , 2011 .