Wilkinson Clare, Lim Rayson B H, Liew Jia Huan, Kwik Jeffrey T B, Tan Claudia L Y, Heok Hui Tan, Yeo Darren C J
National University of Singapore, Singapore, Singapore National University of Singapore Singapore Singapore.
Lingnan University, Hong Kong, China Lingnan University Hong Kong China.
Biodivers Data J. 2022 Sep 14;10:e86192. doi: 10.3897/BDJ.10.e86192. eCollection 2022.
Food webs summarise trophic interactions of the biotic components within an ecosystem, which can influence nutrient dynamics and energy flows, ultimately affecting ecosystem functions and services. Food webs represent the hypothesised trophic links between predators and prey and can be presented as empirical food webs, in which the relative strength/importance of the respective links are quantified. Some common methods used in food web research include gut content analysis (GCA) and stable isotope analysis (SIA). We combine both methods to construct empirical food web models as a basis for monitoring and studying ecosystem-level outcomes of natural (e.g. species turnover in fish assemblage) and intentional environmental change (e.g. biomanipulation).
We present 12 food webs from tropical reservoir communities in Singapore and summarise the topology of each with widely-used network indices (e.g. connectance, link density). Each reservoir was surveyed over 4-6 sampling occasions, during which, representative animal groups (i.e. fish species and taxonomic/functional groups of zooplankton and benthic macroinvertebrates) and all likely sources of primary production (i.e. macrophytes, periphyton, phytoplankton and riparian terrestrial plants) were collected. We analysed gut content in fishes and bulk isotope (dC and dN) profiles of all animals (i.e. fishes and invertebrates) and plants collected. Both sets of information were used to estimate the relative strength of trophic relationships using Bayesian mixing models. We document our protocol here, alongside a script in the R programming language for executing data management/analyses/visualisation procedures used in our study. These data can be used to glean insights into trends in inter- and intra-specific or guild interactions in analogous freshwater lake habitats.
食物网总结了生态系统内生物成分的营养相互作用,这种相互作用会影响养分动态和能量流动,最终影响生态系统的功能和服务。食物网代表了捕食者与猎物之间假设的营养联系,可以呈现为经验性食物网,其中各联系的相对强度/重要性会被量化。食物网研究中使用的一些常见方法包括肠道内容物分析(GCA)和稳定同位素分析(SIA)。我们将这两种方法结合起来构建经验性食物网模型,作为监测和研究自然(如鱼类群落中的物种更替)和人为环境变化(如生物操纵)的生态系统层面结果的基础。
我们展示了来自新加坡热带水库群落的12个食物网,并用广泛使用的网络指标(如连通性、链接密度)总结了每个食物网的拓扑结构。每个水库在4 - 6个采样期内进行了调查,在此期间,收集了代表性动物群体(即鱼类物种以及浮游动物和底栖大型无脊椎动物的分类/功能组)和所有可能的初级生产来源(即大型植物、周丛生物、浮游植物和河岸陆生植物)。我们分析了鱼类的肠道内容物以及所收集的所有动物(即鱼类和无脊椎动物)和植物的整体同位素(δC和δN)特征。这两组信息都用于使用贝叶斯混合模型估计营养关系的相对强度。我们在此记录了我们的方案,以及一个用R编程语言编写的脚本,用于执行我们研究中使用的数据管理/分析/可视化程序。这些数据可用于深入了解类似淡水湖泊栖息地中种间和种内或功能群相互作用的趋势。