(346a) Socio-Ecological Network Structures from Process Graphs in the Context of the Food-Energy-Water Nexus

We have extended the process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species, and we have used the resulting method and theory to show how resilient and water and energy efficient food productions systems can be engineered. 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. A recent extension of the approach to co-culture systems can address food security issues by intensifying production of crops and animal protein without requiring additional land area. We show that graph-theoretic optimization model based on ecological network analysis can determine robust co-culture strategies by controlling the presence of key species. Results of simulations on a hybrid rice and crayfish production system indicate that comparable levels of productivity achieved with different ecological network structures. These are very efficient food production system in terms of energy and water use.

References:

Lao A, Cabezas H, OroszA´ , Friedler F, Tan R (2020) Socio-ecological network structures from process graphs. PLoS ONE 15(8): e0232384. https://doi.org/10.1371/journal.pone.0232384

Lao A, Aviso K, Cabezas H, Tan R (2022) Maintaining the Productivity of Co-culture Systems in the Face of Environmental Change. Nature Sustainability (under review).