Socio-ecological network structures from process graphs

Abstract

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.

Modelling vicious networks with P-graph causality maps

“…In addition, there have been significant recent developments in using P-graph to solve structurally analogous PNS-like problems outside of the traditional process engineering domain (Tan et al 2018 ). For example, P-graph has been used to model economic networks (Aviso et al 2015 ), socio-ecological networks (Lao et al 2020 ), and decision networks (Low et al 2020 ).…”

“…P-graph models can be implemented using the open access platform hosted by the University of Pannonia (P-graph Studio 2020 ), or using a stand-alone implementation such as the Visual Basic for Applications (VBA) program that accompanies the paper by Lao et al ( 2020 ). The former option is used in the case study that follows.…”

Network Modeling for Post-Entry Management of Invasive Pest Species in the Philippines: The Case of the Colorado Potato Beetle, Leptinotarsa decemlineata (Say, 1824) (Coleoptera: Chrysomelidae)

“…It is based on the P-graph framework, a technique originally developed for the computer-aided design of industrial processes [ 13 , 14 ]. Recently, it has been adapted for modeling socioecological networks [ 15 , 16 , 17 ]. The SEP-graph methodology makes use of nodes representing biological species that transform mass and energy flows and produce agricultural goods and services.…”

“…The algorithms underlying the P-graph framework are very general and applicable to a broader class of networks, provided the constraints can be expressed in terms appropriate for the study of the network of interest, ecosystems in our case. Table 1 lists analogous features of the P-graph for process engineering and of the SEP-graph for ecological analysis [ 17 ].…”

“…Table 1. The five original axioms of PNS (left column) [14] and the equivalent formulation for ecosystems (right column) [17].…”