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Population assessment using multivariate time‐series analysis: A case study of rockfishes in Puget Sound

机译:基于多元时间序列分析的种群评估:以普吉特海湾的石鱼为例

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摘要

Estimating a population's growth rate and year‐to‐year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time‐series analysis performed with multivariate autoregressive state‐space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance‐separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set‐up, model selection, and parameter estimation. Our case study is an analysis of the long‐term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery‐independent trawl survey, and recreational fishery surveys affected by bag‐limit reductions. The best‐supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of −3.8% to −3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA‐listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance than the 3.8–3.9% per year declines that were estimated for rockfish populations sampled by the recreational and trawl surveys.
机译:估计人口的增长率和逐年变化是人口生存力分析(PVA)的关键组成部分。但是,标准的PVA方法需要使用多年的一致调查方法获得的时间序列计数。此外,可能难以区分观察和过程差异,这对于PVA至关重要。使用多元自回归状态空间(MARSS)模型执行的时间序列分析是一种灵活的统计框架,它可以解决许多这些局限性。 MARSS模型允许人们将不同齿轮和不同地点的调查结合起来,以估计PVA参数,并实施复制,从而减少了方差分离问题,并最大化了平均趋势估计的信息输入。甚至可以容纳具有未知错误级别的碎片数据。我们提供了一个实际的案例研究,说明了MARSS分析步骤:数据选择,模型设置,模型选择和参数估计。我们的案例研究是根据公民科学水肺调查,独立于渔业的拖网调查以及受袋限制减少影响的休闲渔业调查,对华盛顿州普吉特海湾岩鱼的长期趋势进行的分析。受到最佳支持的模型表明,娱乐和拖网调查追踪了不同的,时间上独立的集合,并且以类似的速度下降(每年平均-3.8%至-3.9%)。水肺调查追踪了一个单独的,数量增加且在时间上独立的组合(每年平均4.1%)。根据美国濒危物种法(ESA),普吉特海湾列出了三种石鱼(博卡乔,金丝雀和黄眼鱼)。这些物种与深水有关,休闲和拖网调查的样本比水肺调查的样本好。在1970年代至2010年代之间,所有在ESA列出的三条石鱼在休闲捕鱼中所占的比例均下降,这表明与经历的休闲娱乐活动和抽样调查所估计的石鱼种群每年所减少的3.8-3.9%相比,它们的丰度下降幅度更大或更相似。拖网调查。

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