Hazard and catch composition of ghost fishing gear revealed by a citizen science clean-up initiative

Ghost fishing, the continued catch of fishes and invertebrates by lost fishing gear, represents an animal welfare issue as well as a waste of both potential food and ecosystem resources. Fishing gear is lost by both commercial and recreational fishers, and management authorities often lack an overview of gear loss and subsequently potential impact on coastal populations. To investigate the hazard and catch composition of lost fishing gear along the Norwegian coast, recreational divers in collaboration with scientists conducted systematic reporting of retrieved lost fishing gear. Through this citizen science project, a total of 12,101 gear items were retrieved and reported, including traps, gillnets and fyke nets. Combining both data on the catch ratio of the gear and its relative quantity, we identified the five most hazardous gear types to be parlor traps, gillnets, fyke nets, wrasse traps and square collapsible traps. The parlour trap was the most hazardous trap, due to high catchabil..., Data was collected by recreational divers when retrieving lost fishing gear along the Norwegian coast between June 2015 – June 2020. All diver reports included a photograph of the retrieved gear, and all photos and forms were quality checked by the authors to check if the three different gear types (traps, gillnets and fyke nets) were correctly identified. All photos of traps were checked and classified into nine different trap-types: parlour trap, round collapsible trap, square collapsible trap, wooden trap, large square trap, circular kelp forest trap, crayfish trap, wrasse trap and other traps. “Other traps” include traps that could not be categorized into the eight trap types, for example, because they were homemade. Data were reported in two different ways within the period. From June 2015 to December 2018, paper forms with pictures were sent to the Norwegian Diving Association and then to the Institute of Marine Research either by mail or e-mail. These reports included depth and G..., All data files are text files.

Gear and trap type, time, depth, coordinates

This dataset contains data on all gear retrieved by divers where the gear type could be verified. The coordinates are given in decimal degrees. They have been reported in different formats and transformed.

Rope, buoy, bottom conditions and slope

This dataset contains data on presence of rope, buoy, bottom conditions and slope by gear type. The data is from all gear retrieved by divers and reported in the app after the app update that allowed divers to supply more detailed reports.

Catch data

This dataset contains data on all reported catches (alive and dead) by gear or trap type. Data herein is retrieved by divers and reported in the app after the app update that allowed divers to supply more detailed reports.

幽灵捕捞（Ghost fishing）指遗失渔具（lost fishing gear）持续捕获鱼类与无脊椎动物的现象，既构成动物福利问题，也造成潜在食物资源与生态系统资源的双重浪费。商业渔民与休闲渔民均可能遗失渔具，而管理机构往往难以掌握渔具遗失情况及其对沿海种群的潜在影响。为探究挪威沿海遗失渔具的危害与渔获组成，休闲潜水员与科研人员合作，对打捞上岸的遗失渔具开展系统性记录。

本公民科学项目（citizen science project）共回收并记录12101件渔具，包括陷阱笼、刺网（gillnets）与地笼网（fyke nets）。结合渔具捕捞率与相对数量数据，研究团队确定了5种高危险性渔具类型：舱式陷阱笼（parlor trap）、刺网、地笼网、濑鱼陷阱（wrasse traps）与方形折叠陷阱笼（square collapsible traps）。其中舱式陷阱笼因极高的捕获率……

数据采集于2015年6月至2020年6月期间，由潜水员在挪威沿海打捞遗失渔具时完成。所有潜水员提交的报告均附带打捞渔具的照片，且所有照片与表单均由研究人员进行质量核查，以确认3类主要渔具（陷阱笼、刺网与地笼网）的识别是否准确。所有陷阱笼照片均经核查并划分为9个类别：舱式陷阱笼、圆形折叠陷阱笼（round collapsible trap）、方形折叠陷阱笼、木质陷阱笼（wooden trap）、大型方形陷阱笼（large square trap）、圆形海藻林陷阱笼（circular kelp forest trap）、小龙虾陷阱笼（crayfish trap）、濑鱼陷阱及其他陷阱笼（"Other traps"）。“其他陷阱笼（"Other traps"）”涵盖无法归入上述8类的渔具，例如自制陷阱笼。

报告数据在研究期间分为两种提交方式：2015年6月至2018年12月期间，潜水员将附带配图的纸质表格提交至挪威潜水协会（Norwegian Diving Association），再由协会通过邮寄或电子邮件转交至挪威海洋研究所（Institute of Marine Research）。此类报告包含水深与……

所有数据文件均为文本格式。

### 渔具与陷阱笼类型、时间、水深、坐标
本数据集包含经核实渔具类型的潜水员打捞渔具数据，坐标以十进制度（decimal degrees）标注，因不同报告格式差异已完成格式转换与统一。

### 绳索、浮标、底质条件与坡度
本数据集包含按渔具类型分类的绳索、浮标、底质条件与坡度相关数据，数据来源于所有经潜水员打捞并在应用程序更新后提交的渔具报告——此次更新允许潜水员提供更详细的报告内容。

### 捕捞数据
本数据集包含按渔具或陷阱笼类型分类的所有已报告渔获（活体与死体）数据，数据由潜水员打捞并在应用程序更新后通过程序提交。