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Identification of high-risk areas for harbour porpoise Phocoena phocoena bycatch using remote electronic monitoring and satellite telemetry data

机译:使用远程电子监测和卫星遥测数据识别海豚海豚的副渔获物高风险区域

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ABSTRACT: The bycatch of harbour porpoise Phocoena phocoena is an issue of major concern for fisheries management and for porpoise conservation. We used high-resolution spatial and temporal data on porpoise abundance and fishing effort from the Danish Skagerrak Sea to identify areas with potentially higher and lower risk of porpoise bycatch. From May 2010 to April 2011, 4 commercial gillnet vessels were equipped with remote electronic monitoring (REM) systems. The REM system recorded time, GPS position and closed-circuit television (CCTV) footage of all gillnet hauls. REM data were used to identify fishing grounds, quantify fishing effort and document harbour porpoise bycatch. Movement data from 66 harbour porpoises equipped with satellite transmitters from 1997 to 2012 were used to model population density. A simple model was constructed to investigate the relationship between the response (number of individuals caught) and porpoise density and fishing effort described by net soak time, net string length and target species. Results showed that a model including both porpoise density and fishing effort data predicted bycatch better than models containing only one factor. We therefore conclude that porpoise telemetry or REM data allow for identification of areas of potential high and low bycatch risk, and better predictions are obtained when combining the 2 sources of data. The final model can thus be used as a tool to identify areas of bycatch risk.
机译:摘要:捕捞海豚 Phocoena phocoena 的副渔获物是渔业管理和海豚保护的主要关注问题。我们使用了关于海豚丰度和丹麦Skagerrak海捕捞努力的高分辨率时空数据,以识别海豚副渔获物的潜在高低风险区域。从2010年5月到2011年4月,有4艘商业刺网船配备了远程电子监控(REM)系统。 REM系统记录了所有刺网运输的时间,GPS位置和闭路电视(CCTV)镜头。 REM数据用于识别渔场,量化捕捞努力并记录海豚的副渔获物。使用1997年至2012年来自66个装有卫星发射器的海豚的运动数据来模拟人口密度。构建了一个简单的模型来研究响应(捕获的个体数量)与海豚密度和捕捞努力之间的关系,并用净浸泡时间,净弦长和目标物种来描述。结果表明,包含海豚密度和捕捞努力数据的模型比仅包含一个因素的模型更好地预测了兼捕。因此,我们得出的结论是,海豚遥测或REM数据可以识别潜在的高和低兼捕风险区域,并且在组合这两种数据源时可以获得更好的预测。因此,最终模型可以用作识别兼捕风险区域的工具。

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