Pocock Michael J O, Schmucki Reto, Bohan David A
UK Centre for Ecology & Hydrology Wallingford, Oxfordshire UK.
Agroécologie, AgroSup Dijon INRAE, Université de Bourgogne Franche-Comté Dijon France.
Ecol Evol. 2021 Aug 25;11(18):12858-12871. doi: 10.1002/ece3.8032. eCollection 2021 Sep.
Ecological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, for example, predation, are short-lived and difficult to observe or detect. While there are different methods for inferring the presence of interactions, they have rarely been used to predict the interaction strengths that are required to construct weighted, or quantitative, ecological networks.Here, we develop a trait-based approach suitable for inferring weighted networks, that is, with varying interaction strengths. We developed the method for seed-feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other species and types of interaction.Using existing literature data from experimental seed-feeding trials, we predicted a per-individual interaction cost index based on carabid and seed size. This was scaled up to the population level to create inferred weighted networks using the abundance of carabids and seeds from empirical samples and energetic intake rates of carabids from the literature. From these weighted networks, we also derived a novel measure of expected predation pressure per seed type per network.This method was applied to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how network structure and predation pressure varied among fields.Inferred networks are valuable because (a) they provide null models for the structuring of food webs to test against empirical species interaction data, for example, DNA analysis of carabid gut regurgitates and (b) they allow weighted networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This permits ecological network analysis even at times and in places when interactions were not directly assessed.
生态网络对生态系统分析很有价值，但由于缺乏数据，其应用常常受到限制，因为许多类型的生态相互作用，例如捕食，持续时间短且难以观察或检测。虽然有不同的方法来推断相互作用的存在，但它们很少被用于预测构建加权或定量生态网络所需的相互作用强度。在这里，我们开发了一种基于特征的方法，适用于推断加权网络，即具有不同的相互作用强度。我们为以种子为食的步甲科地甲虫（鞘翅目：步甲科）开发了该方法，尽管其原理可应用于其他物种和相互作用类型。利用来自种子取食实验的现有文献数据，我们根据步甲和种子大小预测了个体相互作用成本指数。通过将其扩大到种群水平，利用经验样本中步甲和种子的丰度以及文献中步甲的能量摄入率，创建了推断的加权网络。从这些加权网络中，我们还得出了一种新的衡量指标，即每个网络中每种种子类型的预期捕食压力。该方法应用于来自255个耕地的现有生态调查数据，这些数据包括陷阱捕获的步甲数据和种子雨陷阱收集的植物种子数据。对这些推断网络的分析得出了关于不同田地间网络结构和捕食压力如何变化的可检验假设。推断网络很有价值，因为（a）它们为食物网结构提供了零模型，以便与经验性物种相互作用数据进行对比检验，例如对步甲肠道反刍物的DNA分析；（b）只要我们能够估计物种间的相互作用并获得生态普查数据，它们就能使加权网络得以构建。这使得即使在没有直接评估相互作用的时间和地点，也能进行生态网络分析。
