Research ArticleENVIRONMENTAL STUDIES

Wealthy countries dominate industrial fishing

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Science Advances  01 Aug 2018:
Vol. 4, no. 8, eaau2161
DOI: 10.1126/sciadv.aau2161
  • Fig. 1 Distribution of industrial fishing effort by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models.

    (A) The percent of fishing effort (measured in fishing hours) detected globally on the high seas and in all EEZs for vessels flagged to nations from four different World Bank income groups. (B) The percent of AIS-detected industrial fishing effort in all EEZs, grouped by the World Bank income groups of the EEZs. Here, the category Domestic fishing is included, which refers to instances when a fishing country was fishing in its own EEZ. Other categories represent foreign fishing effort conducted within an EEZ by a nation flagged to one of the four World Bank income classes. “Invalid identity” refers to vessels with a Maritime Mobile Service Identity (MMSI) number that did not accurately refer to an individual country. “Unclassified” refers to fishing entities that were fishing in an EEZ but did not have a World Bank income group. All data presented here are summarized from the year 2016.

  • Fig. 2 Density distribution of global industrial fishing effort, derived using AIS data.

    (A) Vessels flagged to higher-income countries and (B) vessels flagged to lower-income countries. Industrial fishing effort is estimated using convolutional neural network models and plotted as the log10 number of fishing hours.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/8/eaau2161/DC1

    Supplementary Materials and Methods

    Fig. S1. Distribution of 2015 industrial fishing effort by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models.

    Fig. S2. Distribution of 2016 industrial fishing effort (measured in fishing days) by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models.

    Fig. S3. Distribution of 2016 industrial fishing effort from vessels flagged to higher- and lower-income nations by high seas ocean basin derived via AIS.

    Fig. S4. Geographic distribution of industrial fishing effort from vessels flagged to higher- and lower-income nations for 2016 derived via the AIS in all countries’ EEZ.

    Fig. S5. Number of vessels for each World Bank income group in FAO registry compared to number of vessels detected through AIS in Global Fishing Watch’s vessel database for vessels >24 m in length.

    Fig. S6. Distribution of 2016 industrial fishing effort (measured in fishing hours) by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models for vessels >12 m in length.

    Fig. S7. Distribution of 2016 industrial fishing effort (measured in fishing hours) by vessels flagged to nations from different income classes using both AIS data and Indonesian VMS data for vessels >24 m.

    Fig. S8. Distribution of 2016 industrial fishing effort (measured in fishing hours) by vessels flagged to nations from different income classes using AIS data, Indonesian VMS data, and corrected low-income and lower middle–income fishing effort for vessels >24 m.

    Table S1. Comparison of top 5 fishing flag states for the high seas, and all high- and low-income EEZs based on AIS-derived effort (total fish hours per fishing state) in 2016 and reconstructed catch (total metric ton caught per fishing state) in 2014 (most recent year of available data).

    Table S2. Top 20 most active fishing flag states on the high seas in 2016.

    Table S3. Top 20 most active fishing states across all EEZs for the year 2016 based on AIS-derived estimates of industrial fishing effort.

    Table S4. Top 20 most active fishing states across all lower-income (lower middle income and low income) EEZs for the year 2016 based on AIS-derived estimates of industrial fishing effort.

    Table S5. Breakdown of countries that have variably codified IMO ratified standards for use of the AIS.

    Table S6. List of countries and other entities used in the analysis and their World Bank income group country classifications (2016).

    Table S7. Amount of fishing effort by Indonesian vessels >24 m from Indonesian VMS data.

    Table S8. Number of vessels >24 m in the FAO registry and detected via AIS for each lower-income country.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Materials and Methods
    • Fig. S1. Distribution of 2015 industrial fishing effort by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models.
    • Fig. S2. Distribution of 2016 industrial fishing effort (measured in fishing days) by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models.
    • Fig. S3. Distribution of 2016 industrial fishing effort from vessels flagged to higher- and lower-income nations by high seas ocean basin derived via AIS.
    • Fig. S4. Geographic distribution of industrial fishing effort from vessels flagged to higher- and lower-income nations for 2016 derived via the AIS in all countries’ EEZ.
    • Fig. S5. Number of vessels for each World Bank income group in FAO registry compared to number of vessels detected through AIS in Global Fishing Watch’s vessel database for vessels >24 m in length.
    • Fig. S6. Distribution of 2016 industrial fishing effort (measured in fishing hours) by vessels flagged to nations from different income classes as measured using AIS data and convolutional neural network models for vessels >12 m in length.
    • Fig. S7. Distribution of 2016 industrial fishing effort (measured in fishing hours) by vessels flagged to nations from different income classes using both AIS data and Indonesian VMS data for vessels >24 m.
    • Fig. S8. Distribution of 2016 industrial fishing effort (measured in fishing hours) by vessels flagged to nations from different income classes using AIS data, Indonesian VMS data, and corrected low-income and lower middle–income fishing effort for vessels >24 m.
    • Table S1. Comparison of top 5 fishing flag states for the high seas, and all high- and low-income EEZs based on AIS-derived effort (total fish hours per fishing state) in 2016 and reconstructed catch (total metric ton caught per fishing state) in 2014 (most recent year of available data).
    • Table S2. Top 20 most active fishing flag states on the high seas in 2016.
    • Table S3. Top 20 most active fishing states across all EEZs for the year 2016 based on AIS-derived estimates of industrial fishing effort.
    • Table S4. Top 20 most active fishing states across all lower-income (lower middle income and low income) EEZs for the year 2016 based on AIS-derived estimates of industrial fishing effort.
    • Table S5. Breakdown of countries that have variably codified IMO ratified standards for use of the AIS.
    • Table S6. List of countries and other entities used in the analysis and their World Bank income group country classifications (2016).
    • Table S7. Amount of fishing effort by Indonesian vessels >24 m from Indonesian VMS data.
    • Table S8. Number of vessels >24 m in the FAO registry and detected via AIS for each lower-income country.

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