Research ArticleAPPLIED ECOLOGY

The environmental niche of the global high seas pelagic longline fleet

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Science Advances  08 Aug 2018:
Vol. 4, no. 8, eaat3681
DOI: 10.1126/sciadv.aat3681
  • Fig. 1 Distribution of global pelagic drifting longline fishing in ABNJ in 2015 and 2016.

    (A) 2015. (B) 2016. Light gray areas depict exclusive economic zones (EEZs) that were excluded from this study. Fishing effort (hours) as calculated by GFW using satellite-based AIS data. Given the differences in quantified fishing effort between 2015 and 2016, the scales were maintained separate to showcase how, despite changes in intensity, the main trends in longline fishing effort are maintained. Gray areas around coastlines depict EEZs excluded from this study. Data are from GFW.

  • Fig. 2 Monthly distribution of pelagic longline fishing effort in ABNJ by the top five fishing States or territories, and all other countries combined.

    The total calculated fishing effort between the years increases between 2015 and 2016, with China and Taiwan experiencing the largest increases in quantified fishing effort. ”*Other” represents a total of 45 other fishing nations deployed longline (LL) gear in ABNJ between 2015 and 2016.

  • Fig. 3 The monthly persistence of suitable habitat in ABNJ for 2015.

    These persistence estimates were calculated using two different probability distribution cutoff thresholds: (A) MPD and (B) ROC. Gray areas around coastlines depict EEZs excluded from this study. Data are from GFW.

  • Fig. 4 The monthly persistence of suitable habitat in ABNJ for 2016.

    These persistence estimates were calculated using two different probability distribution cutoff thresholds: (A) MPD and (B) ROC. Gray areas around coastlines depict EEZs excluded from this study. Data are from GFW.

  • Fig. 5 The average coefficient of variation of predicted high seas fishing suitability for 2015 and 2016.

    Tropical latitudes show, on average, more predictive stability throughout the study period, whereas temperate and subpolar waters show higher degrees of variability of suitable habitat. Gray areas around coastlines depict EEZs excluded from this study. Data are from GFW.

  • Fig. 6 Radar plots of the average quarterly VI scores in 2015 and 2016.

    (A) 2015. (B) 2016. The monthly VI scores for each of the two years assessed were averaged by quarter (Q) to capture the seasonal changes in the importance of each of the environmental predictors: Q1, January–March; Q2, April–June; Q3, July–September; Q4, October–December.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/8/eaat3681/DC1

    Supplementary Materials and Methods

    Fig. S1. The proportion of 2015 and 2016 fishing effort (hours) in ABNJ by gear.

    Fig. S2. The proportion of pelagic longline fishing effort attributed to the main fishing States or territories.

    Fig. S3. Accuracy values obtained for the 2015 and 2016 monthly boosted regression tree models after applying an ROC threshold.

    Fig. S4. Accuracy values obtained for the 2015 and 2016 monthly boosted regression tree models after applying an MPD threshold.

    Fig. S5. The predictive accuracy of the monthly BRTs after projecting them onto future environments.

    Fig. S6. Distribution of predicted and observed fishing effort in January and July of 2015 using different thresholds: ROC and MPD.

    Fig. S7. The SST partial dependence plots from the monthly 2015 models.

    Fig. S8. The temperature at 400-m partial dependence plots from the monthly 2015 models.

    Fig. S9. The DCS partial dependence plots from the monthly 2015 models.

    Fig. S10. The oxygen at 400-m partial dependence plots from the monthly 2016 models.

    Fig. S11. The SST partial dependence plots from the monthly 2015 models.

    Fig. S12. The distribution of fishing effort intensity as a function of the Euclidean distance (kilometers) to the continental shelf.

    Fig. S13. Monthly variable importance scores for boosted regression trees using background pseudoabsence points from the entire high seas areas for 2015 and 2016.

    Table S1. Various model performance indices of the monthly BRTs for 2015 and 2016.

    Table S2. Various model performance indices of the monthly BRTs for 2015 and 2016.

    Table S3. Various model performance indices of the temporally averaged BRT model.

    Table S4. Various model performance indices of the temporally averaged BRT model.

    Table S5. Results from the Wilcoxon signed-rank test comparing the performance of monthly models to the temporally averaged model.

    Table S6. Amount of fundamental niche occupied by pelagic longliners.

    Table S7. The 2015 VI scores.

    Table S8. The 2016 VI scores.

    Table S9. Average 2016 model performance metrics using different environmental variables.

    Table S10. Description of the variable type and source for each of the 14 biophysical and physiographic predictors.

    Table S11. The number of presence and pseudoabsence points in 2015.

    Table S12. The number of presence and pseudoabsence points in 2016.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Materials and Methods
    • Fig. S1. The proportion of 2015 and 2016 fishing effort (hours) in ABNJ by gear.
    • Fig. S2. The proportion of pelagic longline fishing effort attributed to the main fishing States or territories.
    • Fig. S3. Accuracy values obtained for the 2015 and 2016 monthly boosted regression tree models after applying an ROC threshold.
    • Fig. S4. Accuracy values obtained for the 2015 and 2016 monthly boosted regression tree models after applying an MPD threshold.
    • Fig. S5. The predictive accuracy of the monthly BRTs after projecting them onto future environments.
    • Fig. S6. Distribution of predicted and observed fishing effort in January and July of 2015 using different thresholds: ROC and MPD.
    • Fig. S7. The SST partial dependence plots from the monthly 2015 models.
    • Fig. S8. The temperature at 400-m partial dependence plots from the monthly 2015 models.
    • Fig. S9. The DCS partial dependence plots from the monthly 2015 models.
    • Fig. S10. The oxygen at 400-m partial dependence plots from the monthly 2016 models.
    • Fig. S11. The SST partial dependence plots from the monthly 2015 models.
    • Fig. S12. The distribution of fishing effort intensity as a function of the Euclidean distance (kilometers) to the continental shelf.
    • Fig. S13. Monthly variable importance scores for boosted regression trees using background pseudoabsence points from the entire high seas areas for 2015 and 2016.
    • Table S1. Various model performance indices of the monthly BRTs for 2015 and 2016.
    • Table S2. Various model performance indices of the monthly BRTs for 2015 and 2016.
    • Table S3. Various model performance indices of the temporally averaged BRT model.
    • Table S4. Various model performance indices of the temporally averaged BRT model.
    • Table S5. Results from the Wilcoxon signed-rank test comparing the performance of monthly models to the temporally averaged model.
    • Table S6. Amount of fundamental niche occupied by pelagic longliners.
    • Table S7. The 2015 VI scores.
    • Table S8. The 2016 VI scores.
    • Table S9. Average 2016 model performance metrics using different environmental variables.
    • Table S10. Description of the variable type and source for each of the 14 biophysical and physiographic predictors.
    • Table S11. The number of presence and pseudoabsence points in 2015.
    • Table S12. The number of presence and pseudoabsence points in 2016.

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