Food webs as proxies for ecological interaction networks

食物网作为生态相互作用网络的代理

• 批准号: 1655203
• 负责人: George Sugihara
• 金额: $ 40.7万
• 依托单位: University of California-San Diego Scripps Inst of Oceanography
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1655203&HistoricalAwards=false
• 关键词: Food; webs; proxies; ecological; interaction

Historically, food webs have been the common model for ecosystem connectivity and the iconic image of a complex system. By definition, however, food webs depict only direct trophic interactions (who eats whom), one of many ways in which species may interact. This project will develop and use the emerging approach of empirical dynamic modeling (EDM) to construct networks showing the realized causal influences between species (interaction webs) for several ecosystems. These interaction webs are network diagrams that quantify the measurable impact of one species on another as they interact in time. The approach is transformative in that it measures impacts that may be propagated indirectly along causal chains. It is unlike current analytical frameworks, because it accommodates the fact that ecological interactions are not constant but change through time, and depend on the broader ecological context (e.g., where the intensity of competition among consumers changes with prey availability). Moreover, this approach can be used to generate dynamic mathematical models of complex ecosystems from real-time field data. This research has immediate value for producing actionable advice for ecological management by identifying the important species in ecosystems, exposing hidden causal chains of indirect effects (e.g. exposing unintended consequences of management decisions), and by providing scientifically sound early warning signs for critical transitions. The results of this work should be critically useful to resource managers and environmental stewards alike. This project will use the EDM method of Convergent Cross Mapping to identify causal interactions and construct realized influence webs, using data taken from 6 ecosystems spanning 4 biomes: Port Erin Bay, the North Atlantic Ocean, Canada's Experimental Lake Area, and the Chihuahua Desert. These interaction networks will be compared to classical food webs constructed from literature review to test the extent to which food webs depict the most important ecosystem interactions. A cross-system analysis will be done to quantify the type and frequency of non-trophic interactions (e.g. competition versus indirect mutualism), with validation against experimental evidence where possible (e.g. known non-trophic interactions from long-term experiments at two study sites). In addition, the interaction webs will be tested for the ability to identify important nodes and pathways, such as keystone predators and indirect effects resulting from transitive causal chains. Finally, this project will go beyond the classical image of a static ecological network, by acknowledging the reality that interactions are dynamic (i.e. change over time).

从历史上看，食物网一直是生态系统连通性的常见模型，也是复杂系统的标志性形象。然而，根据定义，食物网只描述了直接的营养相互作用（谁吃谁），这是物种相互作用的许多方式之一。本项目将开发和使用经验动态建模（EDM）的新兴方法，以构建网络，显示几个生态系统的物种（相互作用网络）之间实现的因果影响。这些相互作用网络是网络图，量化了一个物种在时间上相互作用时对另一个物种的可测量影响。这种方法具有变革性，因为它衡量的是可能沿着因果链间接传播的影响。它不同于目前的分析框架，因为它适应了这样一个事实，即生态相互作用不是恒定的，而是随着时间的推移而变化，并取决于更广泛的生态背景（例如，消费者之间的竞争强度随着猎物的可获得性而变化）。此外，这种方法可以用来生成动态数学模型的复杂生态系统的实时现场数据。这项研究具有直接的价值，通过确定生态系统中的重要物种，揭示间接影响的隐藏因果链（例如，暴露管理决策的意外后果），并通过提供科学合理的预警信号，为关键过渡生态管理提供可操作的建议。这项工作的结果对资源管理人员和环境管理人员都非常有用。该项目将使用会聚交叉映射的EDM方法来识别因果相互作用并构建已实现的影响网络，使用来自4个生物群落的6个生态系统的数据：埃林湾港，北大西洋，加拿大的实验湖区和奇瓦瓦州沙漠。这些相互作用网络将比较经典的食物网构建的文献综述，以测试在何种程度上食物网描绘最重要的生态系统相互作用。将进行跨系统分析以量化非营养相互作用的类型和频率（例如竞争与间接互利共生），并在可能的情况下根据实验证据进行验证（例如来自长期实验的已知非营养相互作用）两个研究地点）。此外，互动网络将被测试的能力，以确定重要的节点和途径，如关键的捕食者和间接影响所造成的传递因果链。最后，该项目将超越静态生态网络的经典形象，承认相互作用是动态的（即随时间变化）。

项目成果

A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling

基于经验动态建模的生态系统动力学可视化分析方法

• DOI: 10.1109/tvcg.2020.3028956
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Natsukawa, Hiroaki;Deyle, Ethan R.;Pao, Gerald M.;Koyamada, Koji;Sugihara, George
• 通讯作者: Sugihara, George

Frequently asked questions about nonlinear dynamics and empirical dynamic modelling

• DOI: 10.1093/icesjms/fsz209
• 发表时间: 2020-07-01
• 期刊: ICES JOURNAL OF MARINE SCIENCE
• 影响因子: 3.3
• 作者: Munch, Stephan B.;Brias, Antoine;Rogers, Tanya L.
• 通讯作者: Rogers, Tanya L.

Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata

• DOI: 10.1177/1536867x211000030
• 发表时间: 2021-03-01
• 期刊: STATA JOURNAL
• 影响因子: 4.8
• 作者: Li, Jinjing;Zyphur, Michael J.;Laub, Patrick J.
• 通讯作者: Laub, Patrick J.

Comprehensive incentives for reducing Chinook salmon bycatch in the Bering Sea walleye Pollock fishery: Individual tradable encounter credits

减少白令海白眼狭鳕渔业中奇努克鲑鱼兼捕的综合激励措施：个人可交易遭遇积分

• DOI: 10.1016/j.rsma.2018.06.002
• 发表时间: 2018
• 期刊: Regional Studies in Marine Science
• 影响因子: 2.1
• 作者: Sugihara, George;Criddle, Keith R.;McQuown, Mac;Giron-Nava, Alfredo;Deyle, Ethan;James, Chase;Lee, Adrienne;Pao, Gerald;Saberski, Erik;Ye, Hao
• 通讯作者: Ye, Hao

Assessing the predictability of nonlinear dynamics under smooth parameter changes

• DOI: 10.1098/rsif.2019.0627
• 发表时间: 2020-01-01
• 期刊: JOURNAL OF THE ROYAL SOCIETY INTERFACE
• 影响因子: 3.9
• 作者: Cenci, Simone;Medeiros, Lucas P.;Saavedra, Serguei
• 通讯作者: Saavedra, Serguei