Global shark fishing mortality still rising despite widespread regulatory change

Over the last two decades, sharks have been increasingly recognized among the world’s most threatened wildlife, and hence received heightened scientific and regulatory scrutiny. Yet, the effect of protective regulations on shark fishing mortality has not been evaluated at a global scale. Here we estimate that total fishing mortality increased from 76 to 80 million sharks between 2012-2019, ~25 million of which were threatened species. Mortality increased by 4% in coastal waters but decreased 7% in pelagic fisheries, especially across the Atlantic and Western Pacific. By linking fishing mortality data to the global regulatory landscape, we show that widespread legislation designed to prevent shark finning did not reduce mortality, but regional shark fishing or retention bans had some success. These analyses combined with expert interviews highlight evidence-based solutions to reverse the continued overexploitation of sharks., The mortality estimates provided here resulted from the analysis of many datasets, both public and private. All datasets used in this project are detailed in the attached data Inventory. Please refer to the README.md for a brief description of the methods, to the paper Supplemental Methods for in-depth methods, and to the associated software works for the code used to generate the attached datasets. Each dataset is accompanied by a README that provides an overview of the methods used to generate the dataset and a description of dataset variables. , Data files can be opened with Microsoft Excel or Numbers. All code was run using RStudio: 2022.07.0+548 for MacOS an R version 4.1.1, # Title of Dataset: Global shark fishing mortality still rising despite widespread regulatory change

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We estimated spatially explicit global shark mortality for all true sharks and Rhinopristiformes from 2012-2019 at 1x1 degree grids. Total annual mortality was calculated from Regional Fisheries Management Organization (RFMO) shark catch (purse seine and longlines), coastal shark catch (all gear except purse seine and longline), and high seas catch (non-RFMO and unreported RFMO catch). We used machine learning to predict RFMO shark catch risk globally using self- and observer-reported catch and effort data from RFMOs and a suite of environmental parameters. Machine learning allowed us to predict catch across areas with otherwise low reporting coverage. Detailed reconstructions of shark catch in coastal fisheries and the high seas allocated spatially were provided by the Sea Around Us Project. Mortality was calculated at the taxa, gear type, year, and grid cell level for both RFMO c...