All BRTs were fitted to the fishing effort data using RStudio, a development environment for the open-access statistical software R. The models were fitted to the number of monthly fishing effort presence points derived from GFW estimates and double the number background (pseudoabsence) points from the high seas region; a low number of points is recommended for modeling approaches such as BRTs (40). Background points were created on a monthly basis by randomly selecting from the unfished areas of ABNJ (tables S11 and S12). Randomly selecting background pseudoabsence points from anywhere in the high seas, including polar and subpolar regions, where almost no longline fishing effort occurs, biased the results and exaggerated the importance of latitudinally structured variables such as SST and SOC (fig. S13). The distribution of background pseudoabsence points was therefore constrained to areas that had SST values within the observed temperature range of observed fishing. In addition to the 24 monthly models, we computed a temporally averaged model that included the data from all 24 months to assess which of the two approaches performed better. After fitting the classification models, model outputs were mapped onto geographic space by projecting them using layers of the same environmental predictors. The resulting two-dimensional map represents a probability distribution surface where each grid cell in ABNJ was assigned a value between 0 and 1. Confusion matrices were then computed to assess how well each of the monthly models could predict the distribution of longline fishing effort. Various model performance indices were calculated, including the AUC, κ statistic (a measure of categorical agreement describing the difference between the observed and chance agreements), sensitivity (the proportion of actual presence that is accurately predicted), specificity (the proportion of actual absences that are accurately predicted), and accuracy values (tables S1 to S4 and figs. S3 and S4). We then used a nonparametric Wilcoxon signed-rank test to assess whether the performance metrics of the monthly models are statistically dissimilar from those of the temporally averaged model (table S5).

We also explored the explanatory accuracy of the monthly models at predicting the distribution of future fishing effort by projecting monthly models onto the oceanographic conditions 1 (n = 23), 6 (n = 180), and 12 (n = 12) months in advance and assessing how accurately we could predict the distribution of observed longline fishing effort in those months (fig. S5). We further explored the influence of the environmental variables by running two additional monthly models for all the months of 2016, one of which only included static (n = 3) variables and the other was run using only dynamic variables (n = 11) (table S10).

There are multiple possible approaches for selecting a probability distribution threshold to convert probability maps into binary maps. Here, we explored the influence of two separate methods of selecting thresholds for obtaining binary habitat suitability maps. Areas with a monthly probability distribution above the set threshold were considered as suitable habitat for the studied organism. First, we calculated monthly thresholds based on ROC curves, which show the relationship between the true-positive (sensitivity) and false-positive (specificity) rates. The second type of threshold that we calculated was based on the MPD of the monthly models. While both methods are widely accepted procedures (41) for establishing cutoff threshold values, the resulting binary habitat suitability landscapes can differ, and results must not be interpreted as final, but instead as different scenarios of pelagic longline fishing suitability in the high seas. Additional information about how BRTs were fitted and projected is available in Supplementary Materials and Methods.

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