首页> 外文期刊>ICES Journal of Marine Science >Alternative data sources can fill the gaps in data-poor fisheries
【24h】

Alternative data sources can fill the gaps in data-poor fisheries

机译:替代数据源可以填补数据较差的渔业中的差距

获取原文
获取原文并翻译 | 示例
           

摘要

Assessing fish stocks harvested by small-scale fisheries is challenging. The lack of official fisheries data constrains the proper management of such fisheries. Thus, alternative sources of information are crucial to enrich data-poor fisheries. Here, we evaluated different sources of data for the mullet (Mugil liza) fishery, one of the most important but overexploited fisheries in Brazil. We gathered three alternative sources of catch data by artisanal fisheries: 14 years of self-reported catches by artisanal fishers across 24 municipalities; 16 years of catches by traditional beach seines mined from news outlets; and 13 years from a single community monitoring their beach seine catches. We tested whether alternative data sources follow the same trends of landing reports from systematic, official monitoring of the industrial fleet. We fitted Bayesian time-series models to test if environmental changes and stock abundance can predict these data. We found that only self-reported catches matched the official reporting trends, thereby improving our understanding of changes in the mullet stock. These findings reveal that self-reported catches by fishers provide reliable additional data useful for management. Self-reporting data are cost-effective, deals with the complexity of small-scale fisheries, and welcomes fishers as key stakeholders in management practices.
机译:评估小规模渔业收获的鱼类股票挑战。缺乏官方渔业数据限制了这种渔业的适当管理。因此,替代信息来源至关重要,丰富数据较差的渔业。在这里,我们评估了Mullet(Mugil Liza)渔业的不同数据来源,这是巴西最重要但过分的渔业之一。我们通过手工渔业收集了三个替代来源的捕获数据:14年通过24个市的手工渔民自我报告的渔获物; 16年的传统海滩赛道赛因从新闻网点开采;从一个社区监测他们的海滩赛道捕获13年。我们测试了替代数据源是否遵循具有系统正式监测工业舰队的加剧报告的相同趋势。我们适合贝叶斯时间系列模型,以测试环境变化和库存丰度是否可以预测这些数据。我们发现只有自我报告的捕获率匹配了官方报告趋势,从而提高了我们对Mullet股票变化的理解。这些调查结果表明,渔民的自我报告的渔获量提供了可靠的额外数据,可用于管理。自我报告数据具有成本效益,涉及小规模渔业的复杂性,并欢迎渔民作为管理实践中的主要利益相关者。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号