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Application of Bayesian network inference algorithms for foodweb analysis: evaluating the impact of jellyfish predation on Irish Sea plankton

贝叶斯网络推理算法在食物网分析中的应用：评估水母捕食对爱尔兰海浮游生物的影响

基本信息

批准号：
NE/E010350/1
负责人：
Andrew Brierley
金额：
$ 28.76万
依托单位：
University of St Andrews
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2008
资助国家：
英国
起止时间：
2008 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FE010350%2F1
关键词：
Application Bayesian network inference algorithms

项目摘要

Jellyfish (pelagic meduasea of Cnidaria) are voracious plankton predators that can play major roles in structuring pelagic (water-column) marine ecosystems, but aspects of their ecology remain poorly described because of difficulties associated with sampling them using traditional netting techniques. We propose using recently-developed acoustic techniques (multi-frequency scientific echsounding) to estimate abundance of a guild of jellyfish (the Barrel jellyfish, Rhizostoma octopus; the Lion's mane jellyfish Cyanea spp.; the Sea nettle Chrysaora hysoscella, and the Moon jellyfish Aurelia aurita) in a region of the Irish Sea identified previously by aerial surveys as a Rhizostoma 'hot spot'. We will also sample non-gelatinous zooplankton (eg copepods, amphipods and fish larvae that are prey for jellyfish) using vertically-fished nets so that we can describe quantitatively the composition of the plankton (jellyfish 'food') under conditions of varying jellyfish abundance. We expect, in general terms, that there will be fewer zooplankton prey items in regions where jellyfish abundance is highest because jellyfish will have captured some zooplankton. Because there are numerous possible links in the foodweb via which zooplankton could be consumed (zooplankton may consume each other in addition to being consumed by jellyfish), we will apply powerful Bayesian network inference algorithms to multiple sets of regionally-naturally-varying jellyfish abundance and zooplankton abundance data to infer the most likely foodweb, and thus the impact by jellyfish on the plankton community. This will greatly improve our understanding of the predatory impact of jellyfish, and will provide insight to possible ecosystem consequences (eg to fisheries recruitment) of increasing jellyfish abundance following, for example, fishery-driven finfish decline or environmental change. It has been suggested that jellyfish will proliferate in the face of high fishing pressure because fishing removes fish that are competitors with jellyfish for plankton: less fish means more food for jellyfish. Once we have a robust foodweb model, we will use it in reverse with historic zooplankton abundance data (collected by the Continuous Plankton Recorder, a long-term zooplankton sampling programme run opportunistically from commercial ships) to reconstruct the likely changes over time in jellyfish abundance in the Irish Sea from the impact any changes in their abundance would have had on the zooplankton community. Herring stocks in the Irish Sea collapsed between 1972 and 1980 and, in a kind of ecological archaeology, we will look for clues in possible changing plankton community composition over that time for increases in jellyfish abundance. Although it has been suggested that jellyfish may proliferate following finfish decline (a consequence of so-called 'fishing down the foodweb') there is little direct evidence of this because time-series of jellyfish abundance are scarce (jellyfish do not have hard parts so, for example, leave little trace in sediments once they die). If we are able to reveal an historic link between jellyfish abundance and fish abundance this will be a very useful advance for managers seeking to regulate fisheries in an ecosystem context. The project will also demonstrate to a wide ecologist audience the power of inference algorithms for foodweb analysis.

水母是一种贪婪的浮游生物捕食者，在构建远洋（水柱）海洋生态系统中发挥着重要作用，但由于使用传统的渔网技术对它们进行采样存在困难，因此对它们的生态学方面的描述仍然很差。我们建议使用最近开发的声学技术（多频率科学回声）来估计爱尔兰海地区水母（桶状水母，根口章鱼，狮子鬃毛水母，海荨麻和月亮水母）的丰度，这些水母之前被航空调查确定为根口“热点”。我们还将使用垂直渔网对非胶状浮游动物（如桡足类、片脚类和水母捕食的鱼类幼虫）进行取样，这样我们就可以在水母数量变化的情况下定量描述浮游生物（水母的“食物”）的组成。我们预计，一般来说，在水母数量最多的地区，浮游动物的猎物会更少，因为水母会捕获一些浮游动物。由于在食物网中有许多浮游动物被消耗的可能环节（浮游动物除了被水母消耗之外还可能相互消耗），我们将应用强大的贝叶斯网络推理算法对多组区域自然变化的水母丰度和浮游动物丰度数据进行推断，以推断最可能的食物网，从而推断水母对浮游生物群落的影响。这将极大地提高我们对水母捕食影响的理解，并将提供对水母数量增加可能造成的生态系统后果（如渔业招聘）的见解，例如，渔业驱动的鱼类数量减少或环境变化。有人认为，水母会在高捕捞压力下繁殖，因为捕捞会消除与水母竞争浮游生物的鱼类：鱼类减少意味着水母的食物更多。一旦我们有了一个强大的食物网模型，我们将把它与历史上的浮游动物丰度数据（由连续浮游生物记录仪收集，这是一个长期的浮游动物采样计划，从商业船只上偶然运行）反向使用，从它们丰度的任何变化对浮游动物群落的影响中，重建爱尔兰海水母丰度随时间的可能变化。1972年至1980年间，爱尔兰海的鲱鱼种群急剧减少，我们将以一种生态考古学的方式，从那段时间浮游生物群落组成的可能变化中寻找线索，以寻找水母数量增加的线索。尽管有人认为，水母可能会随着鳍鱼数量的减少而繁殖（这是所谓的“沿着食物网捕鱼”的结果），但几乎没有直接证据证明这一点，因为水母数量的时间序列是稀缺的（水母没有坚硬的部分，因此，例如，一旦它们死亡，在沉积物中留下的痕迹很少）。如果我们能够揭示水母数量和鱼类数量之间的历史联系，这将是一个非常有用的进步，为管理者寻求在生态系统背景下调节渔业。该项目还将向广大生态学家展示食物网分析推理算法的力量。