COVID-19 lockdowns reveal the resilience of Adriatic Sea fisheries to forced fishing effort reduction

[1]  A. Mancini,et al.  AIS data, a mine of information on trawling fleet mobility in the Mediterranean Sea , 2021, Marine Policy.

[2]  N. Macfarlane,et al.  The relative importance of COVID‐19 pandemic impacts on biodiversity conservation globally , 2021, Conservation biology : the journal of the Society for Conservation Biology.

[3]  M. Coll,et al.  Ecological and economic effects of COVID-19 in marine fisheries from the Northwestern Mediterranean Sea , 2021, Biological Conservation.

[4]  J. Olden,et al.  A global perspective on the influence of the COVID-19 pandemic on freshwater fish biodiversity , 2021, Biological Conservation.

[5]  M. Picone,et al.  The good, the bad and the ugly of COVID-19 lockdown effects on wildlife conservation: Insights from the first European locked down country , 2020, Biological Conservation.

[6]  Jessica A. Gephart,et al.  Emerging COVID-19 impacts, responses, and lessons for building resilience in the seafood system , 2020, Global Food Security.

[7]  Keon Myung Lee,et al.  Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data , 2020, Applied Sciences.

[8]  Elena M. Finkbeiner,et al.  The COVID-19 Pandemic, Small-Scale Fisheries and Coastal Fishing Communities , 2020 .

[9]  Stan Matwin,et al.  Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning , 2020, Canadian Conference on AI.

[10]  Athanassios C. Tsikliras,et al.  Estimating stock status from relative abundance and resilience , 2019, ICES Journal of Marine Science.

[11]  Adriano Mancini,et al.  A Cloud Computing Architecture to Map Trawling Activities Using Positioning Data , 2019, Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications.

[12]  A. Soldo,et al.  Characteristics of the Croatian anchovy purse seiner fleet , 2019, Acta Adriatica.

[13]  Shay O'Farrell,et al.  Classifying fishing behavioral diversity using high-frequency movement data , 2019, Proceedings of the National Academy of Sciences.

[14]  P. Pepin,et al.  The Northwest Atlantic Fisheries Organization Roadmap for the development and implementation of an Ecosystem Approach to Fisheries: structure, state of development, and challenges , 2019, Marine Policy.

[15]  O. R. Eigaard,et al.  Bottom trawl fishing footprints on the world’s continental shelves , 2018, Proceedings of the National Academy of Sciences.

[16]  O. Defeo,et al.  Operationalizing an ecosystem approach to small-scale fisheries in developing countries: The case of Uruguay , 2018, Marine Policy.

[17]  Gianna Fabi,et al.  Mapping change in bottom trawling activity in the Mediterranean Sea through AIS data , 2018, Marine Policy.

[18]  Gianpaolo Coro,et al.  Status and rebuilding of European fisheries , 2018, Marine Policy.

[19]  Françoise Gourmelon,et al.  How can Automatic Identification System (AIS) data be used for maritime spatial planning? , 2018, Ocean & Coastal Management.

[20]  A. Jarre,et al.  Applying a decision tree framework in support of an ecosystem approach to fisheries: IndiSeas indicators in the North Sea , 2018 .

[21]  Michel J. Kaiser,et al.  A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints , 2018 .

[22]  Luky Adrianto,et al.  Review of national laws and regulation in Indonesia in relation to an ecosystem approach to fisheries management , 2018 .

[23]  Jahn Petter Johnsen,et al.  Reconstructing governability: How fisheries are made governable , 2018 .

[24]  Barbara A. Block,et al.  Tracking the global footprint of fisheries , 2018, Science.

[25]  S. Libralato,et al.  Fish and fishery historical data since the 19th century in the Adriatic Sea, Mediterranean , 2017, Scientific Data.

[26]  K. Kleisner,et al.  Estimating Fisheries Reference Points from Catch and Resilience , 2017 .

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

[28]  Francois Bastardie,et al.  Spatial planning for fisheries in the Northern Adriatic: working toward viable and sustainable fishing , 2017 .

[29]  Pasquale Pagano,et al.  Analysing and forecasting fisheries time series: purse seine in Indian Ocean as a case study , 2016 .

[30]  Andrea Belardinelli,et al.  Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities , 2016 .

[31]  Paul Woods,et al.  Global Fishing Watch: Bringing Transparency to Global Commercial Fisheries , 2016, ArXiv.

[32]  Stan Matwin,et al.  Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning , 2016, PloS one.

[33]  Fabrizio Natale,et al.  Mapping EU fishing activities using ship tracking data , 2016, ArXiv.

[34]  N. Bailly,et al.  Estimating absence locations of marine species from data of scientific surveys in OBIS , 2016 .

[35]  Brian Sullivan,et al.  Ending hide and seek at sea , 2016, Science.

[36]  J. Hentati‐Sundberg,et al.  Classifying fishers' behaviour. An invitation to fishing styles , 2016 .

[37]  P. Pagano,et al.  Automatic classification of climate change effects on marine species distributions in 2050 using the AquaMaps model , 2016, Environmental and Ecological Statistics.

[38]  Fabrizio Natale,et al.  Mapping Fishing Effort through AIS Data , 2015, PloS one.

[39]  Christopher J. Smith,et al.  The Seascape of Demersal Fish Nursery Areas in the North Mediterranean Sea, a First Step Towards the Implementation of Spatial Planning for Trawl Fisheries , 2015, PloS one.

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

[41]  Divesh Srivastava,et al.  Data quality: The other face of Big Data , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[42]  L. Boitani,et al.  Update or Outdate: Long‐Term Viability of the IUCN Red List , 2014 .

[43]  Elizabeth A. Fulton,et al.  An Integrated Approach Is Needed for Ecosystem Based Fisheries Management: Insights from Ecosystem-Level Management Strategy Evaluation , 2014, PloS one.

[44]  F. Foglini,et al.  Bathymetry of the Adriatic Sea: The legacy of the last eustatic cycle and the impact of modern sediment dispersal , 2014 .

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

[46]  Pasquale Pagano,et al.  Deriving fishing monthly effort and caught species from vessel trajectories , 2013, 2013 MTS/IEEE OCEANS - Bergen.

[47]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

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

[49]  J. Company,et al.  Ploughing the deep sea floor , 2012, Nature.

[50]  Michel J. Kaiser,et al.  Implications of using alternative methods of vessel monitoring system (VMS) data analysis to describ , 2012 .

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

[52]  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 .

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

[54]  Per Erik Bergh,et al.  Fishery Monitoring, Control and Surveillance , 2009 .

[55]  Brendan J. Godley,et al.  A Step Towards Seascape Scale Conservation: Using Vessel Monitoring Systems (VMS) to Map Fishing Activity , 2007, PloS one.

[56]  Michael Hoffmann,et al.  The value of the IUCN Red List for conservation. , 2006, Trends in ecology & evolution.

[57]  Saša Raicevich,et al.  Rapido trawling in the northern Adriatic Sea: effects on benthic communities in an experimental area , 2000 .

[58]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[59]  J. Olsen,et al.  The European Commission , 2020, The European Union.

[60]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[61]  P. Pagano,et al.  An Open Science approach to infer fishing activity pressure on stocks and biodiversity from vessel tracking data , 2021, Ecol. Informatics.

[62]  A. Glance The State of Mediterranean and Black Sea Fisheries 2020 , 2020 .

[63]  G. Coro OPEN SCIENCE AND ARTIFICIAL INTELLIGENCE SUPPORTING BLUE GROWTH , 2020, Environmental Engineering and Management Journal.

[64]  J. Adams,et al.  Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review , 2016 .

[65]  John Icely,et al.  A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems , 2015 .

[66]  Colm Lordan,et al.  Integrating vessel monitoring systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution , 2011 .

[67]  D. Pauly,et al.  Top Predators in Marine Ecosystems: Effects of fisheries on ecosystems: just another top predator? , 2006 .

[68]  F. Grassle The Ocean Biogeographic Information System (OBIS): An On-line, Worldwide Atlas for Accessing, Modeling and Mapping Marine Biological Data in a Multidimensional Geographic Context , 2000 .

[69]  J. Fleiss Measuring nominal scale agreement among many raters. , 1971 .