Vessel monitoring systems (VMS) reveal an increase in fishing efficiency following regulatory changes in a demersal longline fishery

Abstract A global expansion of satellite-based monitoring is making fisher behavioral responses to management actions increasingly observable. However, such data have been underutilized in evaluating the impacts of fishing on target and non-target fish stocks or the ramifications of management strategies on fishers. We demonstrate how vessel monitoring system (VMS) data can provide a suite of metrics (such as effort) for improving inputs to stock assessments, dynamic delineation of fishing grounds, and evaluation of regulatory or other (e.g., climatic) impacts on fisher performance. Using >1 million VMS records from the Gulf of Mexico grouper-tilefish demersal longline fishery, we first develop a generalized additive modeling approach that predicts fishing duration with ∼85% accuracy. We combine model predictions with logbook data to compare the fishery before and after implementation of a suite of regulatory changes (e.g., a shift to catch share management). We find a large-scale reduction in fleet size, accompanied by reduced fishing effort (duration * number of hooks), shorter trips, lower operational expenses, higher catch rates, and more earnings for those vessels that remained in the fishery. We discuss how the combination of VMS and associated metrics can be expanded for use in management strategy evaluation, parameterizing economic models of fisher behavior, improving fishery-dependent stock assessment indices, and deriving socioeconomic indicators in fisheries worldwide.

[1]  CharlesColin,et al.  Using hidden Markov models to infer vessel activities in the snow crab (Chionoecetes opilio) fixed gear fishery and their application to catch standardization , 2014 .

[2]  A. Haynie,et al.  Using Vessel Monitoring System Data to Identify and Characterize Trips Made by Fishing Vessels in the United States North Pacific , 2016, PloS one.

[3]  Michel J. Kaiser,et al.  Quantification and prediction of the impact of fishing on epifaunal communities , 2011 .

[4]  E. Pante,et al.  marmap: A Package for Importing, Plotting and Analyzing Bathymetric and Topographic Data in R , 2013, PloS one.

[5]  R. Sinnott.  Virtues of the Haversine , 1984 .

[6]  N. Farmer,et al.  Stock Complexes for Fisheries Management in the Gulf of Mexico , 2016, Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science.

[7]  Ronan Fablet,et al.  Defining fishing spatial strategies from VMS data: Insights from the world's largest monospecific fishery , 2015 .

[8]  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 .

[9]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[10]  Guido Schwarzer,et al.  Easier parallel computing in R with snowfall and sfCluster , 2009, R J..

[11]  Nicholas D. Ducharme-Barth,et al.  Classification and analysis of VMS data in vertical line fisheries: Incorporating uncertainty into spatial distributions , 2017 .

[12]  H. Lassen,et al.  Manual on estimation of selectivity for gillnet and longline gears in abundance surveys , 2000 .

[13]  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 .

[14]  A. Haynie,et al.  The effect of decreasing seasonal sea-ice cover on the winter Bering Sea pollock fishery , 2012 .

[15]  Michael J. Fogarty,et al.  Lagged social-ecological responses to climate and range shifts in fisheries , 2012, Climatic Change.

[16]  David C. Smith,et al.  Human behaviour: the key source of uncertainty in fisheries management , 2011 .

[17]  Etienne Rivot,et al.  Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models , 2010 .

[18]  E. Thunberg,et al.  Performance of federally managed catch share fisheries in the United States , 2016 .

[19]  R. Grafton,et al.  Individual transferable quotas: theory and practice , 1996, Reviews in Fish Biology and Fisheries.

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

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

[22]  S. Pascoe,et al.  Theories and behavioural drivers underlying fleet dynamics models , 2012 .

[23]  Steven J. D. Martell,et al.  Can catch share fisheries better track management targets , 2012 .

[24]  Larry Perruso,et al.  Improving detection of short-duration fishing behaviour in vessel tracks by feature engineering of training data , 2017 .

[25]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

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

[27]  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 .

[28]  John F. Walter,et al.  Modelling age-dependent movement: an application to red and gag groupers in the Gulf of Mexico , 2015 .

[29]  Etienne Rivot,et al.  An autoregressive model to describe fishing vessel movement and activity , 2015 .

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

[31]  Alain F. Zuur,et al.  A protocol for data exploration to avoid common statistical problems , 2010 .

[32]  A. Haynie,et al.  Paths to resilience: the walleye pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea , 2018, Canadian Journal of Fisheries and Aquatic Sciences.

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

[34]  T. Essington,et al.  Ecological indicators display reduced variation in North American catch share fisheries , 2009, Proceedings of the National Academy of Sciences.

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

[36]  David Peel,et al.  A hidden Markov model approach for determining vessel activity from vessel monitoring system data , 2011 .

[37]  Judith P. Gocke,et al.  Descriptions of the U.S. Gulf of Mexico Reef Fish Bottom Longline and Vertical Line Fisheries Based on Observer Data , 2011 .

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

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

[40]  E. Schwaab,et al.  Final amendment 31 to the fishery management plan for reef fish resources in the Gulf of Mexico , 2010 .

[41]  Carl J. Walters,et al.  Adaptive Management of Renewable Resources , 1986 .

[42]  K M Conrad,et al.  Threats to Internal Validity in Worksite Health Promotion Program Research: Common Problems and Possible Solutions , 1991, American journal of health promotion : AJHP.

[43]  A. Fernö,et al.  Responses of cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) to baited hooks in the natural environment , 1989 .

[44]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

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

[46]  Martin D. Smith,et al.  Catch shares slow the race to fish , 2017, Nature.

[47]  Patricia M. Clay,et al.  Measuring the social and economic performance of catch share programs: Definition of metrics and application to the U.S. Northeast Region groundfish fishery , 2014 .

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