Beckett Stephen J, Boulton Chris A, Williams Hywel T P
College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QE,, UK.
F1000Res. 2014 Aug 6;3:185. doi: 10.12688/f1000research.4831.1. eCollection 2014.
Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an "adaptive ensemble" method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts.
嵌套性是一种统计量度，用于在多种生态和进化背景下解释二分相互作用数据，例如生物地理学（物种 - 地点关系）和物种相互作用（植物 - 传粉者以及宿主 - 寄生虫网络）。已经使用了多种方法来评估嵌套性，这些方法在确定嵌套性指标的方式上有所不同。此外，还使用了几种不同的零模型来计算嵌套性得分的统计显著性。大量的量度和零模型，其中许多给出相互矛盾的结果，这对于不同研究之间嵌套性的比较来说是个问题。我们开发了FALCON软件包，以便使用当前文献中一些更流行的量度和零模型，对给定输入网络的嵌套性得分和统计显著性进行轻松而高效的比较。FALCON目前包括用于二元网络嵌套性的六种量度和五种零模型，以及用于加权网络嵌套性的两种量度和四种零模型。FALCON软件旨在高效且易于使用。FALCON代码以三种语言（R、MATLAB、Octave）提供，并且设计为模块化和可扩展的，使用户能够通过添加更多量度和零模型轻松扩展其功能。FALCON提供了一种强大的方法，可使用多种量度和零模型来比较给定二分网络中嵌套性的强度和显著性。它包括一种“自适应集成”方法，用于在计算统计显著性时减少零分布的欠采样。它可以处理二元或加权输入网络。FALCON是对文献中不同嵌套性量度及相关零模型激增的一种回应。它允许使用不同方法轻松而高效地计算嵌套性得分和统计显著性，从而能够比较不同研究的结果，进而支持对不同生物学背景下嵌套性的成因及影响进行理论研究。
