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A KALMAN FILTER APPROACH TO ESTIMATING MARINE TURTLE INCIDENTAL TAKE RISK

机译:估计海龟偶然的卡尔曼过滤方法承担风险

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A principle U.S. fisheries management concern is ensuring compliance with Endangered Species Act requirements to limit incidental takes of listed species to levels which do not result in potential jeopardy to existing population stocks. Part of the risk assessment process involves an analysis of historic takes (bycatch) of ESA-listed species relative to fishing effort. Sample bycatch per unit of effort (BPUE) ratios are used to gauge the take risk to ESA-listed species posed by future fishing effort.The rare-event nature of protected species takes complicates the interpretation of sample BPUE ratios. For illustration, a total of 23 leatherback turtles were incidentally taken by fishermen in the California-Oregon drift gillnet fishery from the 1990through 2006 fishing seasons in 7733 sets of fishing effort, with from 0 to 5 leatherback takes per season over the entire period. Should one conclude the incidental take risk was significantly higher during the season with 5 leatherback turtle takes than in seasons with 0 takes? To address this and related inference problems, we specify a state space model which describes the pattern of observed takes as a Poisson process conditional on the level of fishing effort. A time-varying Poisson rate parameter, expressed as BPUE, measures the time-varying level of take risk. A Kalman filter for Poisson processes is used to estimate the intertemporal variation in take risk from California-Oregon drift gillnet fishery observer data. A key question regards attributing the observed pattern of takes to randomness which drives fluctuations in observed takes versus intertemporal variation in the level of take risk.
机译:美国渔业管理问题的原则是确保遵守濒危物种法案的要求,以将所列物种的偶然收入限制在不导致现有人口股的潜在危险的水平。部分风险评估过程涉及相对于捕捞努力的历史性历史性历史(兼捕)的物种。每单位努力(BPUE)比率的兼容样品用于衡量未来捕捞努力所构成的ESA列出的物种的风险。受保护物种的罕见事件性质使对样品BPUE比的解释使其复杂化。出于插图,共有23龟在加州 - 俄勒冈州的渔业渔业渔业中偶然采取了23龟,从2006年的钓鱼季节捕捞季节,在钓鱼努力方面的钓鱼季节,从0到5座棱皮抬头,在整个时期都需要每季。如果一个结束偶然承担风险在赛季中有5个皮革龟龟比0季节占用0次占有率为了解决此问题和相关推理问题,我们指定了一个状态空间模型,该模型描述了观察到的模式,作为泊钓努力水平的泊松过程条件。表示为BPUE的时变泊松率参数,测量时变风险水平。用于泊松进程的卡尔曼滤波器用于估计来自加州 - 俄勒冈州漂移Gillnet渔业观察者数据的跨现经变化。归因于归因于观察到的随机性模式,这是驱动所观察到的波动的随机性,与跨度变化发生在风险的水平。

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