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使用多元自回归模型(MAR)分析上旧金山湾浮游物种的减少。

Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR).

机构信息

Australian Centre for Biodiversity, School of Biological Sciences, Monash University, Melbourne 3800, Australia.

出版信息

Ecol Appl. 2010 Jul;20(5):1417-30. doi: 10.1890/09-1724.1.

Abstract

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2 per thousand isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey): Our results were relatively robust with respect to the form of stock-recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state-space models that describe more fully the life-history dynamics of the declining species.

摘要

在美国加利福尼亚州旧金山河口上游,有四种洄游性鱼类受到了特别的管理关注,自大约 2002 年以来,它们的数量急剧下降:尖吻鲈(Hypomesus transpacificus)、长鳍拟沙丁鱼(Spirinchus thaleichthys)、条纹鲈(Morone saxatilis)和银汉鱼(Dorosoma petenense)。自 20 世纪 60 年代末以来,该河口一直受到监测,收集了大量关于鱼类、它们的洄游性猎物、浮游植物生物量、入侵物种和物理因素的数据。我们使用多元自回归(MAR)模型来辨别导致这些鱼类下降的主要因素。建立了一个专家启发式模型来描述这个系统。该模型中有 54 个关系,其中只有一个关系在事先是不确定的。在所提出的关系中,有 28 个得到了强有力的支持或与数据一致,而有 26 个关系接近零(数据不支持,但也不是预期的那样)。2‰等盐线(衡量河口对淡水流量的物理响应的指标)的位置和分析期间水的清澈度增加是影响多个下降类群(包括鱼类和鱼类的主要浮游动物猎物)的两个因素:我们的结果相对于使用的种群补充模型的形式和包括辅助协变量是相对稳健的,但通过使用更全面地描述下降物种的生活史动态的详细状态空间模型,结果可能会得到增强。

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